tinyml_V5_summary

Summary of “TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers”

Authors: Pete Warden & Daniel Situnayake
Publisher: O’Reilly Media, Inc.
Publication Date: December 2019 (First Edition)

Overview

“TinyML” focuses on implementing machine learning (ML) on ultra-low-power microcontrollers using TensorFlow Lite. It explores the integration of ML models with hardware like Arduino, SparkFun Edge, and STM32F746G Discovery Kit. The book is structured to guide readers from understanding ML basics to deploying complex models on microcontrollers.

Key Topics

Introduction to Embedded Devices and ML

• Embedded Devices: The book begins by explaining the role and evolution of embedded devices in the ML landscape.
• Machine Learning Basics: It introduces core ML concepts, including the deep learning workflow, model training, and inference.

Getting Started

• Target Audience: Designed for engineers and hobbyists with an interest in ML and embedded systems.
• Hardware and Software Requirements: Details on necessary tools, such as Python, Jupyter Notebooks, TensorFlow, and Arduino IDE.

Building and Training Models

• Model Development: The process includes defining model architecture, training, and converting models to TensorFlow Lite.
• Hello World Example: A step-by-step guide on building and deploying a basic ML model on microcontrollers.

Application Development

• Wake-Word Detection: Covers building and deploying a model to recognize specific spoken commands.
• Person Detection: Instructions for creating a model to detect human presence using image data.
• Magic Wand Application: Utilizes accelerometer data to recognize gestures, demonstrating ML application versatility.

Advanced Topics

• TensorFlow Lite for Microcontrollers: Discusses adapting TensorFlow models for microcontroller constraints.
• Optimization Techniques: Strategies for improving latency, energy usage, and reducing model size.
• Debugging and Deployment: Offers insights into troubleshooting and transitioning from development to production environments.

Privacy and Security

• Design Considerations: Emphasizes the importance of privacy and secure deployment of ML models.
• Protection Strategies: Techniques for safeguarding models and their data.

Learning and Resources

• Further Learning: Encourages engagement with the TinyML Foundation and other ML communities.
• Additional Tools: Provides appendices on generating Arduino libraries and capturing audio data.

Conclusion

“TinyML” is a comprehensive guide for deploying machine learning on low-power devices, emphasizing practical applications and optimization. It serves as a valuable resource for those interested in the intersection of machine learning and embedded systems, offering both theoretical knowledge and hands-on projects.

Summary

TinyML Overview
TinyML represents the integration of machine learning (ML) models on embedded devices, enabling them to process data locally with minimal energy consumption. This concept allows simple algorithms on small, energy-efficient chips to perform tasks traditionally requiring more powerful hardware. The essence of TinyML lies in transforming raw sensor data into actionable insights directly on the device, avoiding the high energy costs of data transmission.

Book’s Objective
The book aims to guide developers with basic command-line and coding experience in creating ML projects on embedded systems. It introduces readers to the world of TinyML, where neural networks operate at energy costs below 1 milliwatt, enabling applications to run on small batteries for extended periods without human intervention.

Target Audience
The book is crafted for individuals new to both ML and embedded software development. It provides step-by-step instructions, making it accessible to those with minimal technical background. The goal is to empower readers to build and customize TinyML applications, such as speech recognition or gesture detection, for their specific needs.

Hardware and Software Requirements
To get started, a computer with a USB port and an embedded development board with sensors like microphones, accelerometers, or cameras is needed. The book emphasizes using affordable hardware, with development kits available for under $30. Training ML models is facilitated through Google Colab, ensuring accessibility without high-end computing resources.

Development Environment
Embedded devices often operate under resource constraints, typically featuring limited RAM and flash memory. They may lack standard operating systems, requiring developers to adapt to unique environments. However, the simplicity of these systems can make understanding and optimizing processes more straightforward.

Changing Landscape
The field of TinyML is rapidly evolving, with advancements in hardware and software. The book provides a snapshot of the current state (as of 2019) while offering links to updated resources and examples. It focuses on foundational skills like debugging and model creation, ensuring readers can adapt to future developments.

Acknowledgments
The authors express gratitude to various contributors and support teams, highlighting the collaborative effort required to produce the book. Special thanks are given to individuals and teams from the TensorFlow community and other pioneering projects in embedded ML.

Conclusion
The book serves as a comprehensive guide to developing embedded ML products, encouraging innovation in applications that leverage the unique capabilities of TinyML. It seeks to inspire readers to create impactful solutions in various domains, from consumer electronics to environmental monitoring.

For additional resources and updates, readers are directed to the book’s website and O’Reilly’s online learning platform.

The book collaborates with Ambiq and SparkFun to produce the $15 SparkFun Edge board, with examples compatible with this device. The second version, SparkFun Edge 2, will also support all projects, albeit with slight variations in code and instructions. Each chapter links to a README.md for updated deployment instructions. Projects can also run on Arduino Nano 33 BLE Sense and STM32F746G Discovery kit for Mbed, adaptable to other devices if sensor data is captured correctly. The book’s source code is on GitHub, updated to support additional devices, with README.md files listing supported devices and deployment instructions.

Projects require no additional components except for person detection, which needs a camera module. For Arduino, use Arducam Mini 2MP Plus, and for SparkFun Edge, use SparkFun’s Himax HM01B0 breakout. Projects utilize TensorFlow Lite for Microcontrollers, a variant designed for embedded devices with limited memory. The code is open source and evolves with optimizations and bug fixes. Although code may differ from the book’s print, the core principles remain.

A code editor like Microsoft’s VS Code is recommended, working across macOS, Linux, and Windows. Command entry is via terminal on macOS/Linux or Command Prompt on Windows. Additional software is required for board communication, with Python needed for build scripts on SparkFun Edge or Mbed devices, and GNU Screen or Tera Term for debug logging. Arduino boards have necessary tools in the IDE package.

The book aims to foster applications in TinyML, introducing domain experts to problem-solving with machine learning on embedded systems. It provides an overview of current capabilities and future possibilities, enabling readers to build and modify practical examples using time-series data and low-power vision. It emphasizes a whole-system perspective, considering energy consumption across components, and focuses on practical use and modification of components rather than deep technical dives.

Machine learning is introduced as a tool for solving diverse problems, accessible without needing a PhD. It demystifies ML, focusing on using it with tiny devices, covering basic concepts, tools, and training a simple model. The book covers machine learning fundamentals, the types of problems it solves, key terms, and the workflow for deep learning problem-solving.

Deep learning is explained as a method for predicting outcomes based on past data, with models trained to identify patterns. The book outlines a workflow: defining goals, collecting datasets, designing model architecture, training models, converting models, running inference, and evaluating results. It emphasizes the importance of relevant data collection, diversity in datasets, and correct data labeling for effective model training.

Overall, the book aims to equip readers with the knowledge to participate in design discussions and prototype solutions using machine learning on embedded systems.

Summary

In machine learning, supervised learning involves training a model by associating data with predefined classes, such as “normal” and “abnormal.” This process, known as labeling, is crucial for teaching the model to classify inputs accurately. For time-series data, labeling requires identifying periods of normal and abnormal operation, which may involve experimentation to determine accurately.

Data Collection and Labeling: Data sources in a time-series format include rate of production, temperature, and vibration, each logged at different intervals. Labels are recorded every 10 seconds, matching the smallest interval, ensuring each data point can be easily labeled.

Model Design: Designing a deep learning model involves choosing or creating an architecture suitable for the problem and data type. This decision considers the device constraints, such as memory and processing power, and may involve starting with a simple model and iteratively refining it. Models process data in the form of tensors, which are multi-dimensional arrays.

Feature Generation: Transforming time-series data into a tensor requires generating features. This involves techniques like windowing, where data within a specified time frame is averaged to create a single set of values. The window is labeled based on the presence of abnormal labels within it, forming a vector of features for model input.

Normalization: Input data is normalized to ensure floating-point values are similar in size, ideally between 0 and 1. This is achieved by adjusting data values, such as subtracting the mean from each feature or scaling image pixel values.

Training the Model: Training involves feeding data through the model, adjusting its parameters (weights and biases) using backpropagation to minimize loss and maximize accuracy. Training continues until the model converges, indicated by stabilized performance metrics. Hyperparameters, such as the number of epochs and neurons, are tuned to optimize performance.

Challenges: Models can suffer from underfitting, where they fail to learn patterns, or overfitting, where they learn too specific patterns, reducing generalization. Achieving a balance is crucial for model effectiveness.

Iterative Process: Machine learning is iterative, involving repeated cycles of data collection, feature design, model training, and evaluation. The goal is to refine the model until it performs satisfactorily or determine if the task is too complex for current techniques.

In conclusion, machine learning involves a comprehensive workflow from data labeling to model training, with iterative refinement to achieve reliable predictions, even if not perfectly accurate.

Summary

Overfitting in Models

Overfitting occurs when a model learns its training data too well, failing to generalize to new data. This often happens because the model memorizes the training data or relies on shortcuts not present in real-world scenarios. For example, if a model learns to classify animals based on background settings rather than features of the animals themselves, it may fail on new data.

Techniques to Combat Overfitting

• Model Size Reduction: Decreasing model size limits its capacity to memorize training data.
• Regularization: Techniques like L1, L2 regularization, and dropout constrain model complexity.
• Data Augmentation: Generates new, artificial data points by modifying existing data to expand the dataset.
• Larger Datasets: Acquiring more varied data is often the best solution.

Training, Validation, and Testing

To ensure a model’s effectiveness, datasets are split into training, validation, and test sets (typically 60%, 20%, 20%). The training data is used to fit the model, while validation data helps monitor overfitting. Testing confirms the model’s generalization ability. Monitoring loss and accuracy on validation data helps detect overfitting.

Model Deployment with TensorFlow Lite

For deploying models on microcontrollers, TensorFlow Lite is used. Models are converted to a format suitable for low-powered devices using TensorFlow Lite Converter, which can optimize models for size and speed.

Running Inference and Application Integration

Once converted, models can be loaded on devices using TensorFlow Lite for Microcontrollers. Inference involves transforming sensor data into a format the model understands, running predictions, and interpreting the output scores to determine actions, such as detecting abnormal states in machinery.

Evaluating and Troubleshooting

Real-world performance may differ due to environmental factors or overfitting. Troubleshooting involves checking hardware, comparing real-world data with the training set, and potentially retraining models with new data. Regularization and data augmentation can help improve performance.

Building and Training a Model

The chapter provides a hands-on approach to building a simple machine learning model using Python, TensorFlow, and Google Colaboratory. The example involves training a model to predict sine wave values and deploying it on microcontrollers to control LEDs.

Tools and Setup

The project uses Python and Jupyter Notebooks for coding and experimentation. The practical application involves deploying models on devices like the Arduino Nano 33 BLE Sense and SparkFun Edge, demonstrating the integration of machine learning with hardware.

Overall, the chapter emphasizes understanding the deep learning workflow, tackling overfitting, and deploying models efficiently on constrained devices.

Summary

Jupyter Notebooks and Colab

Jupyter Notebooks is a versatile document format that integrates writing, graphics, and executable code, making it ideal for exploring machine learning problems. We will use Google Colaboratory (Colab), a free online platform that runs Jupyter Notebooks on Google’s hardware, facilitating easy sharing and consistent results across different users.

TensorFlow and Keras

TensorFlow is a comprehensive toolset for machine learning, initially developed by Google and now widely used in the industry. We will use Keras, TensorFlow’s high-level API, to build and train deep learning models, and TensorFlow Lite to deploy models on mobile and embedded devices.

Building the Model

Setup

We will build, train, and convert our model using a Jupyter Notebook. The process begins by loading the notebook into Colab, which allows us to run and edit the code interactively.

Importing Dependencies

The first step involves importing necessary libraries: TensorFlow, NumPy, Matplotlib, and Python’s math library. These libraries support machine learning operations, data manipulation, and visualization.

Generating Data

The model will be trained on data generated by a sine function. We will create 1,000 random data points along a sine wave and add noise to simulate real-world data. This is crucial for training the model to recognize patterns amidst noise.

Data Visualization

Using Matplotlib, we will visualize the generated data. This helps in understanding the data distribution and is a critical step in the machine learning workflow.

Splitting the Data

The dataset will be divided into training (60%), validation (20%), and test (20%) sets. This ensures that the model is evaluated on fresh data not used during training, improving its generalization.

Model Definition

We will create a simple regression model using Keras. The model consists of a Sequential architecture with two layers:

1. First Layer: A dense layer with 16 neurons using the ReLU activation function. It processes the input scalar value.
2. Final Layer: A single neuron layer to output the predicted value.

The model is compiled with the RMSprop optimizer and mean squared error (MSE) loss function, suitable for regression tasks.

Conclusion

This setup provides a robust framework for building and training machine learning models, leveraging the power of TensorFlow and the convenience of Colab. The process includes generating and visualizing data, splitting datasets, and defining a neural network model using Keras. This foundational approach is suitable for beginners and scalable for more complex deep learning tasks.

Summary

The text provides an overview of building and training a neural network model using Keras, focusing on the ReLU activation function and its role in enabling the network to model complex nonlinear relationships. ReLU, a simple function that returns the maximum of zero and its input, is crucial for allowing multiple layers of neurons to work together effectively. Without activation functions, networks would be limited to modeling linear relationships, which would not suffice for complex patterns like sine waves.

The model architecture described consists of two layers: the first with 16 neurons using ReLU activation, and the second with a single neuron as the output layer. The model’s output is computed by multiplying inputs with weights, summing the results, and adding a bias. The weights and biases are learned during training.

The compile() step in Keras configures the optimizer, loss function, and metrics for training. The optimizer, rmsprop, adjusts the network during training. The loss function, mse (mean squared error), measures prediction accuracy, and mae (mean absolute error) is used as a performance metric.

To train the model, the fit() method is used, specifying training data, epochs, batch size, and validation data. The training process involves running data through the network multiple times (epochs) and updating weights after processing batches of data. The batch size balances training efficiency and model accuracy, with a common choice being 16 or 32.

Training logs provide insight into the model’s performance, showing loss and error metrics over epochs. Initially, the model improves, but overfitting can occur if the training loss is significantly lower than validation loss. Graphs of training and validation loss and mean absolute error help visualize the network’s learning progress and identify issues like overfitting.

The text concludes that the initial model is too simplistic to accurately capture the sine wave’s complexity, as evidenced by linear predictions. To improve performance, increasing the model’s size or complexity is suggested, which is a typical step in refining machine learning models.

Overall, the document highlights the iterative nature of designing, training, and evaluating machine learning models to achieve better predictive accuracy.

In this text, the focus is on enhancing a neural network’s performance by adding layers and optimizing its architecture using TensorFlow and TensorFlow Lite. Initially, a model is expanded by adding a layer of 16 neurons, increasing its complexity and parameter count from 49 to 321, a 555% increase. This added complexity aims to better represent the data’s intricacies.

The model is compiled using the ‘rmsprop’ optimizer and ‘mse’ loss function, with ‘mae’ as a metric. Training is conducted over 600 epochs with improved validation loss and mean absolute error metrics, indicating reduced overfitting and better performance.

Graphs are used to visualize training and validation losses, as well as mean absolute error, confirming the model’s improvement. The validation metrics outperform training metrics because validation occurs post-epoch, leveraging slightly more training.

A crucial step involves testing with a reserved 20% of data to ensure the model generalizes well and isn’t overfitting. The test results show minimal deviation from validation metrics, confirming the model’s robustness.

The text introduces TensorFlow Lite, a toolset for deploying models on edge devices. It involves converting models into a space-efficient format using the TensorFlow Lite Converter, which can apply optimizations like quantization. Quantization reduces model size by converting weights from 32-bit floats to 8-bit integers, enhancing execution speed with minimal accuracy loss.

Two versions of the model are created: one in the TensorFlow Lite FlatBuffer format and another quantized. The TensorFlow Lite Interpreter, although more complex than Keras, is used to make predictions, demonstrating the models’ accuracy post-conversion.

The quantized model is notably smaller, saving 224 bytes, though the reduction is minor due to the model’s already small size. This reduction is more significant in larger models.

Finally, the converted model is prepared for deployment on microcontrollers, which typically lack filesystems. The model is transformed into a C source file for integration into applications, ensuring efficient execution on constrained devices.

This process exemplifies the steps needed to scale and optimize machine learning models for deployment on resource-limited hardware, maintaining performance while minimizing size and execution time.

Summary

Model Integration

To efficiently integrate a trained model into a project, the model is provided as a C source file, defined as an array of bytes. This allows it to be included in the binary and loaded directly into memory. The Unix tool xxd is used to convert the model into this format. The converted model is saved as sine_model_quantized.cc, which can be included in a project either by copying the source or downloading the file.

Building a TinyML Application

A model alone cannot function without being wrapped in an application. Chapter 5 focuses on building an embedded application using the sine model to control an LED or an LCD display. The application is written in C++ 11, designed to be minimalistic for educational purposes. This chapter explains the code structure necessary for running a TensorFlow Lite model on microcontrollers, making it a useful template for future projects.

Writing and Running Tests

Before diving into application code, writing tests is recommended. Tests verify that the code performs as expected and serve as examples of implementation. The hello_world_test.cc file is an example that loads the model, runs inference, and checks predictions. The code includes necessary dependencies and sets up a testing framework using macros like TF_LITE_MICRO_TESTS_BEGIN.

Code Structure and Dependencies

The code includes several dependencies:

• sine_model_data.h: Contains the model data.
• all_ops_resolver.h: Provides operations for the model.
• micro_error_reporter.h: Logs errors and debug information.
• micro_interpreter.h: Runs the model.
• micro_test.h: Framework for writing tests.
• schema_generated.h: Defines the TensorFlow Lite FlatBuffer schema.
• version.h: Contains the schema version number.

Setting Up Logging and Model Mapping

A MicroErrorReporter instance is created for logging. The model data is mapped into a usable structure using GetModel(), and its version is checked against the TensorFlow Lite schema version to ensure compatibility.

Creating an AllOpsResolver and Tensor Arena

An AllOpsResolver instance is created to provide the necessary operations for the interpreter. A tensor arena is allocated as an array of bytes to store input, output, and intermediate tensors. The size of the tensor arena may require adjustments through trial and error to fit the model’s needs while conserving memory.

Conclusion

The chapter emphasizes the importance of understanding the general structure of a TensorFlow Lite for Microcontrollers program. It provides a foundational understanding of how to integrate, test, and run a model in a TinyML application, preparing readers for more advanced implementations in future projects.

Summary of TinyML Application Development

Setting Up the Interpreter

To execute a model with TensorFlow Lite for Microcontrollers, a MicroInterpreter is created, which is crucial for running models on microcontrollers. Memory allocation for model tensors is done using the AllocateTensors() method, which assigns memory from tensor_arena to each tensor. This step is essential before running inference.

Input Tensor Inspection

After setting up the interpreter, input data is provided to the model’s input tensor. The input tensor is accessed using the input() method of the interpreter. Tensor properties are verified using TensorFlow Lite for Microcontrollers’ testing framework macros like TF_LITE_MICRO_EXPECT_NE and TF_LITE_MICRO_EXPECT_EQ. These macros ensure that the input tensor has the expected properties, such as dimensions and data type.

Running Inference

To run inference, a value is assigned to the input tensor, and the model is invoked using interpreter.Invoke(). The status of the inference is checked to ensure it ran successfully. The TfLitePtrUnion union is used to store different data types in tensors, allowing for flexible data handling.

Handling Complex Inputs

For models accepting more complex inputs, such as vectors or matrices, values are assigned sequentially in memory. The tensor’s dimensions are managed internally, allowing for multidimensional data handling.

Output Tensor Management

The model’s output is accessed similarly to the input, using a TfLiteTensor. Its properties are verified to ensure it matches expectations. The output value is then extracted and checked using TF_LITE_MICRO_EXPECT_NEAR to confirm it is within an acceptable range of the expected result, accounting for model approximation and floating-point calculation errors.

Testing and Validation

Inference is run multiple times with different inputs to validate the model’s functionality. The same input and output tensor pointers are reused for efficiency. Successful tests confirm that TensorFlow Lite for Microcontrollers can load models, allocate tensors, run inference, and return expected results.

Running Tests Locally

Although the code is designed for microcontrollers, it can be tested on development machines for easier debugging. This involves writing logic in tests that can be run locally before deploying to hardware. Essential tools include a terminal emulator, a bash shell, Git, and Make. These tools facilitate code compilation and testing on a development machine, streamlining the development process.

Tools and Environment Setup

To run tests locally, ensure Git and Make are installed. Verify their installation by checking their versions in the terminal. If missing, search for installation instructions specific to your operating system. Once set up, you can compile and run tests locally, enhancing the development workflow for embedded applications.

Summary

This guide provides a detailed walkthrough for running tests and understanding the file structure of a TensorFlow Lite for Microcontrollers application, focusing on a “Hello World” example.

Setting Up TensorFlow

1. Cloning the Repository: Start by cloning the TensorFlow source code using Git: bash git clone https://github.com/tensorflow/tensorflow.git cd tensorflow

2. Using Make for Tests: The Make tool automates build tasks. To run tests, navigate to the root of the TensorFlow directory and use: bash make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test

   This command builds and runs the hello_world_test.

3. Test Output: Successful tests will display:

    
   Introducing errors in the test file (`hello_world_test.cc`) will show failed tests, providing useful debugging information.

Project File Structure

The application is located in tensorflow/lite/micro/examples/hello_world and includes:

• BUILD: Lists buildable components.
• Makefile.inc: Defines build targets.
• README.md: Instructions for building and running the application.
• constants.h/.cc: Contains program constants.
• create_sine_model.ipynb: Jupyter notebook for model creation.
• hello_world_test.cc: Test for model inference.
• main.cc: Entry point of the program.
• main_functions.h/.cc: Defines setup() and loop() functions for initialization and core logic.
• output_handler.h/.cc: Handles output display, customizable per device.
• sine_model_data.h/.cc: Defines the model data array.

Subdirectories like arduino/, disco_f76ng/, and sparkfun_edge/ contain device-specific implementations.

Walking Through the Source Code

1. main_functions.cc: Core logic includes:

   • Global Variables: Declares TensorFlow objects and a tensor_arena for memory.
   • setup() Function: Initializes logging, model, interpreter, and allocates memory.
   • loop() Function: Calculates x values, runs inference, and handles output using HandleOutput().

2. output_handler.cc: Default implementation logs x and y values. Custom implementations are provided for different platforms.

3. main.cc: Contains the main() function, the entry point for the program, ensuring the loop() function runs repeatedly.

This setup demonstrates running inference in a loop, showcasing TensorFlow Lite for Microcontrollers’ capabilities in a simple application.

Summary

The text provides a detailed guide on building and deploying a “Hello World” application using TensorFlow Lite for Microcontrollers. It begins by explaining the structure of a microcontroller application, highlighting the use of setup() and loop() functions within a main() function. This loop runs indefinitely, which is typical for microcontroller applications that perform continuous tasks without multitasking.

Building and Running the Application

To test the application, it needs to be built using a Make command that creates an executable binary. The instructions differ based on the operating system (macOS, Linux, Windows). Once executed, the application outputs logs displaying x_value and y_value in a power-of-two format, which is efficient for microcontrollers lacking hardware support for floating-point operations.

Deploying to Microcontrollers

The text transitions to deploying the application on three devices: Arduino Nano 33 BLE Sense, SparkFun Edge, and ST Microelectronics STM32F746G Discovery kit. Each device has unique output capabilities, such as LEDs or LCD displays, requiring custom implementations of the HandleOutput() function.

Understanding Microcontrollers

Microcontrollers are introduced as key components on boards like Arduino, SparkFun Edge, and STM32F746G. They connect to the circuit board via pins, which can be for power, input/output of digital signals, or analog inputs. GPIO pins are highlighted for their role in input/output operations.

Arduino Specifics

The Arduino Nano 33 BLE Sense is recommended due to its compatibility with TensorFlow Lite and additional features like a microphone and accelerometer. The text explains how to handle output on Arduino using PWM (Pulse Width Modulation) to vary LED brightness based on the sine wave prediction. The HandleOutput() function adjusts LED brightness, logs the value, and uses Arduino’s serial interface for data communication.

Running the Example on Arduino

To run the example, the Arduino IDE is used to install the TensorFlow Lite Arduino library and load the hello_world example. Differences in the Arduino example code are noted, such as automatic calling of setup() and loop() functions and file extensions. The text provides instructions for selecting the correct board and port in the Arduino IDE, compiling, and uploading the code to the device.

Once uploaded, the LED on the Arduino board should fade or flash, demonstrating on-device ML. The Serial Plotter in the Arduino IDE can be used to graph the brightness value over time, providing a visual representation of the sine wave prediction.

This comprehensive guide serves as an introduction to deploying machine learning models on microcontrollers, with a focus on practical implementation and understanding of hardware-specific considerations.

Summary

The text provides a comprehensive guide on deploying and modifying a TinyML application on the SparkFun Edge development board, which is designed for machine learning on tiny devices. It emphasizes the use of the Ambiq Apollo 3 microcontroller, featuring an Arm Cortex M4 processor core, and outlines steps for working with the board’s four LEDs to visualize sine wave outputs.

Key Concepts:

• Arduino Integration: Users can modify and deploy applications using the Arduino IDE. Experiments include adjusting LED blink rates and logging animations to the serial port.

• SparkFun Edge Overview: The board is optimized for power efficiency and includes four LEDs for output visualization. The LEDs are used to indicate different value ranges of a sine wave.

• Output Handling: The output_handler.cc file is critical for managing LED outputs based on the sine wave’s y-values. The setup involves configuring GPIO pins using the Ambiq Apollo3 SDK to control the LEDs.

• Code Implementation: The HandleOutput() function initializes LEDs and adjusts them based on positive or negative y-values. The function logs x and y values to the serial port using a custom ErrorReporter.

Deployment Steps:

1. Setup: Install Python 3 and dependencies like pycrypto and pyserial. Clone the TensorFlow repository and navigate to the directory.

2. Building the Binary: Use a Makefile to compile the binary for the SparkFun Edge, ensuring dependencies are downloaded.

3. Signing the Binary: Sign the binary with cryptographic keys using scripts from the Ambiq SDK. This prepares the binary for flashing onto the device.

4. Flashing the Device: Connect the SparkFun Edge to a USB programmer and use a shell script to flash the signed binary. Ensure the board is in bootloader mode during this process.

5. Testing and Debugging: After flashing, reset the board to see the LEDs in action. If issues arise, troubleshooting steps include ensuring proper button sequence during flashing and verifying connections.

6. Viewing Debug Data: Use a terminal command to monitor the board’s serial port output, providing insight into the program’s operation and inference results.

The guide concludes with tips for troubleshooting common issues during flashing and deployment, ensuring users can effectively implement and test their TinyML applications on the SparkFun Edge.

Summary of TinyML Deployment and Wake-Word Detection

TinyML Deployment on Microcontrollers

SparkFun Edge Board:

• Use CoolTerm on Windows to listen to data logged via the serial port.
• Modify application code in tensorflow/lite/micro/examples/hello_world to change LED blink rates or log animations.

STM32F746G Discovery Kit:

• Features an Arm Cortex-M7 processor and runs Arm’s Mbed OS.
• Use the LCD for visual animations, with x-axis for inferences and y-axis for predictions.
• Adjust kInferencesPerCycle for animation speed.
• Output handling code is in hello_world/disco_f746ng/output_handler.cc.
• Includes methods to control the LCD and set colors using hex values.
• Deployment involves using the Mbed toolchain and configuring the project for C++11.

Building and Running:

• Requires STM32F746G board, USB cable, Arm Mbed CLI, Python 3, and pip.
• Use TensorFlow Lite Makefile to generate a directory for Mbed.
• Configure Mbed to use the project root and download dependencies.
• Compile with mbed compile -m DISCO_F746NG -t GCC_ARM.
• Deploy by copying the binary to the STM board.

Wake-Word Detection with TinyML

Overview:

• Digital assistants like Google Assistant and Alexa use wake-word detection for privacy and efficiency.
• TinyML enables low-powered devices to detect wake words locally, saving bandwidth and battery.

Application:

• Builds an application using an 18 KB model to recognize “yes” and “no”.
• Uses a microphone to listen and indicate detection via LEDs or screen.
• Application architecture involves obtaining input, preprocessing, running inference, and using output.

Devices Supported:

• Arduino Nano 33 BLE Sense
• SparkFun Edge
• STM32F746G Discovery kit

Key Concepts:

• Cascading: Using a small model to trigger a larger one.
• Provides privacy and speed by performing inference offline.

Conclusion

This chapter demonstrates deploying TinyML models to microcontrollers and implementing wake-word detection. It highlights the efficiency and privacy benefits of using TinyML for always-on voice recognition applications.

Wake-Word Application Complexity

The wake-word application involves significant complexity due to several factors: it processes audio data as input, utilizes a classifier model to output class probabilities, continuously performs inference on live data, and employs a larger, more complex model that pushes hardware limits.

Model Overview

The model is trained to recognize “yes” and “no,” distinguish unknown words, and filter out silence or background noise. It uses the Speech Commands dataset, which includes 65,000 utterances of 30 words. The model categorizes input into four classes: “yes,” “no,” unknown words, and silence. It processes one second of data at a time and outputs probability scores for each class.

Spectrograms and CNNs

Instead of raw audio, the model uses spectrograms—two-dimensional arrays representing frequency information over time. This preprocessing simplifies the model’s task. Spectrograms are fed as 2D tensors into a convolutional neural network (CNN), which is adept at handling multidimensional data and identifying patterns, making it suitable for spectrogram analysis.

Application Architecture

The application consists of several components:

• Main Loop: Runs continuously, executing processes as fast as possible.
• Audio Provider: Captures raw audio data, customizable per device.
• Feature Provider: Converts raw audio into spectrograms on a rolling basis.
• TF Lite Interpreter: Runs the model, transforming input spectrograms into probabilities.
• Model: Included as a data array, executed by the interpreter.
• Command Recognizer: Aggregates inference results to determine if a known word was detected.
• Command Responder: Uses device output capabilities to notify the user of detected commands.

Testing Components

Tests are essential for understanding the application:

• micro_speech_test.cc: Demonstrates inference on spectrogram data.
• audio_provider_test.cc: Shows audio provider usage.
• feature_provider_mock_test.cc: Uses a mock audio provider.
• recognize_commands_test.cc: Interprets model output.
• command_responder_test.cc: Triggers device output.

Basic Flow and Tensor Allocation

The test micro_speech_test.cc follows a familiar flow: loading the model, setting up the interpreter, and allocating tensors. Unlike simpler examples, it uses a MicroMutableOpResolver to include only necessary operations, optimizing memory usage.

Audio Provider

The audio provider interfaces with device microphones, returning 16-bit PCM audio data. Developers can implement it for various platforms. The core function, GetAudioSamples(), returns audio data and its size.

Feature Provider

The feature provider converts audio samples into spectrograms for the model. It operates during the main loop and fills memory with feature data, maintaining accessibility for its lifetime.

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This summary captures the key components and processes involved in developing a wake-word detection application, highlighting the model’s structure, application architecture, and testing methodologies.

The text discusses the implementation and testing of a feature provider for audio data used in wake-word detection applications. The feature provider is responsible for converting audio into spectrogram data, which is then used for inference in a machine learning model.

Feature Provider and Mock Testing

• Feature Provider: Converts audio into spectrogram data, representing one second of audio as a 2D array with 40 columns and 49 rows. Each row corresponds to a 30-ms audio sample analyzed by a fast Fourier transform (FFT), producing 43 frequency buckets.
• Mock Testing: Uses a mock audio provider for testing, which simulates audio input. The tests are defined in feature_provider_mock_test.cc and use audio_provider_mock.cc for mock data.

Key Functions

• PopulateFeatureData: Updates the spectrogram data array with new slices of audio data. It compares the last call time with the current time, processes the audio, and updates the array with new data while discarding old data beyond one second.
• FFT Processing: The FFT is applied to 49 overlapping 30-ms slices of audio to build the spectrogram, as managed by micro_features_generator.cc.

RecognizeCommands Class

• Purpose: Determines if a word was detected by analyzing the model’s output probabilities. It averages the results over a set window to account for variations in audio input.
• ProcessLatestResults: This method processes the model’s output, ensuring the input tensor is correct and adding results to an averaging window. It checks for a valid detection by comparing scores against a threshold and ensuring sufficient time has passed since the last detection.

Testing and Implementation

• Tests: Conducted in recognize_commands_test.cc, these tests validate the feature provider and RecognizeCommands functionality by simulating various scenarios.
• Implementation Details: The feature provider optimizes for embedded platforms by minimizing dependencies to keep the binary size small, crucial for devices with limited memory.

Conclusion

The feature provider and RecognizeCommands class work together to convert audio into spectrograms and determine word detection. This system is essential for applications like wake-word detection, where accuracy and efficiency are critical. The mock testing setup ensures robust validation of the feature provider’s functionality.

Summary

This text provides a detailed walkthrough of implementing a wake-word detection application using TensorFlow Lite for Microcontrollers. The main components include setting up and testing the RecognizeCommands class, implementing a Command Responder, and deploying the application to various devices, including microcontrollers like the Arduino Nano 33 BLE Sense.

RecognizeCommands Class

The RecognizeCommands class is responsible for processing audio input to detect specific commands. It uses a tensor containing fake inference results to test the ProcessLatestResults() function, ensuring it returns kTfLiteOk when successful. The class is tested using a command that runs all tests to verify its functionality.

Command Responder

The Command Responder is designed to output detection results. It is implemented in command_responder.cc and logs detection results as text. The function RespondToCommand() takes parameters like error_reporter, current_time, found_command, score, and is_new_command. It is called every time inference is performed, even if no command is detected. A simple test ensures the function’s callability without crashing.

Main Functions

The main_functions.cc file contains the core setup() and loop() functions. The setup involves loading the model, setting up the interpreter, adding operations, and allocating tensors. It checks the input tensor’s shape and type to ensure compatibility. The loop function continuously fetches audio spectrograms, runs the model, and interprets the output using the RecognizeCommands instance. It calls RespondToCommand() to notify users of detected commands.

Running and Deploying the Application

The application can be run on a Mac using a built-in audio provider. It detects words like “yes” and “no” and outputs results with scores and timestamps. For deployment on microcontrollers, specific implementations of audio_provider.cc and command_responder.cc are needed for each device. The text provides a walkthrough for the Arduino Nano 33 BLE Sense, which uses a built-in microphone and LED for output.

Arduino Implementation

The Arduino implementation uses the built-in LED to indicate command detection. The RespondToCommand() function toggles the LED with each inference and keeps it on for three seconds if “yes” is detected. The setup requires the Arduino IDE and the TensorFlow Lite Arduino library.

Conclusion

The text outlines the integration of TensorFlow Lite for Microcontrollers into a wake-word detection system, detailing the setup, testing, and deployment processes. It emphasizes the modularity and adaptability of the system across different hardware platforms, providing a comprehensive guide for developers interested in embedded machine learning applications.

Summary

This text provides detailed instructions for deploying and testing a wake-word detection application on microcontrollers using Arduino and SparkFun Edge. It begins with setting up the micro_speech example in the Arduino IDE, which involves selecting the correct device type and port, and uploading the code to the Arduino device. The example detects the word “yes,” lighting an LED for confirmation. The text notes that the model may struggle with accuracy due to its small size, particularly in distinguishing “no” from “unknown.”

Modifying the Application

Users are encouraged to experiment with the code, such as switching the LED response to “no” instead of “yes,” creating a sequence of commands, or using commands to control other components like LEDs or servos.

SparkFun Edge Setup

The SparkFun Edge board features a microphone and four colored LEDs. The text explains how to toggle LEDs based on voice commands: blue for toggling, yellow for “yes,” red for “no,” and green for unknown commands. The setup involves configuring the pins connected to the LEDs and using functions from the Apollo3 SDK to control them.

Building and Deploying the Code

To deploy the application on the SparkFun Edge, users need to:

1. Clone the TensorFlow repository and navigate to its directory.
2. Build the binary using the make command.
3. Sign the binary with cryptographic keys using scripts from the Ambiq SDK.
4. Flash the binary to the device using a USB programmer like the SparkFun Serial Basic Breakout.

The process includes setting the device into bootloader mode, running a script to flash the binary, and ensuring the device name is correctly identified.

Testing and Debugging

After flashing, the program is tested by pressing the RST button and issuing voice commands. The LEDs should respond accordingly, with the blue LED flashing to indicate the program is running. Debugging tips are provided for common issues, such as flashing errors or unresponsive LEDs.

Viewing Debug Data

The program logs successful recognitions to the serial port, which can be monitored using a baud rate of 115200. Users can view debug information by issuing commands and checking the serial output.

Overall, the text provides a comprehensive guide to deploying a wake-word detection application on microcontrollers, highlighting both the setup process and potential troubleshooting steps.

Summary of Wake-Word Detection and Deployment

Overview

The text provides a comprehensive guide on deploying and modifying a wake-word detection application using the TensorFlow Lite framework on microcontrollers, specifically the STM32F746G Discovery Kit. It covers the setup, code modification, deployment, and testing processes, as well as training a new model for different wake words.

Application Deployment

1. Basic Setup:

   • The application code is located in tensorflow/lite/micro/examples/micro_speech.
   • Users can modify this code to create custom functionalities, such as using LEDs to indicate prediction scores or controlling other components with “yes” and “no” commands.

2. STM32F746G Features:

   • The kit includes an LCD display used to show detected commands, with different background colors for “yes,” “no,” “unknown,” and “silence.”

3. RespondToCommand Function:

   • Logs detected commands to the serial port.
   • Uses lcd.Clear() and lcd.DisplayStringAt() to update the display based on the command received.

4. Deployment Steps:

   • Requires an STM32F746G board, a mini-USB cable, Arm Mbed CLI, Python 3, and pip.
   • Use the Makefile to generate a suitable directory for Mbed.
   • Commands include mbed config root ., mbed deploy, and mbed compile -m DISCO_F746NG -t GCC_ARM.
   • Deploy the compiled binary to the STM32F746G board.

5. Testing:

   • Test the application by saying “yes” or “no” and observing the display changes.
   • Debug information is available via a serial connection at a baud rate of 9600.

Modifying and Extending the Application

• Suggestions for modifications include visual score indicators and creating a secret code phrase with command sequences.
• The application captures data, processes it into features, and feeds it into the model for inference.

Training a New Model

1. Colaboratory (Colab) Usage:

   • Google Colab is recommended for training due to its access to powerful GPUs.
   • The training script allows customization of input words, preprocessing, and model architecture.

2. Model Configuration:

   • The WANTED_WORDS environment variable specifies the words for model training.
   • Example configuration for training on “on” and “off” commands.

3. Training Process:

   • The script downloads a dataset, trains a model, and outputs a file for TensorFlow Lite.
   • Training involves configuring parameters like TRAINING_STEPS and LEARNING_RATE.

4. Model Integration:

   • The newly trained model can be integrated into the existing application code.
   • The inference part of the application is small, focusing on capturing and processing sensor data.

Conclusion

The guide emphasizes the ease of deploying and modifying machine learning models on microcontrollers. It highlights the importance of preprocessing and efficient model integration for successful wake-word detection applications. Future chapters will cover training custom models and optimizing them for microcontroller environments.

Summary of Wake-Word Detection Training Process

Overview

The process of training a wake-word detection model involves several key steps, including setting up training parameters, installing dependencies, monitoring with TensorBoard, and finally deploying the model. This summary covers these steps in detail, highlighting essential information for effective model training and deployment.

Training Parameters

• Training Steps and Learning Rate: The model undergoes 18,000 training steps, initially with a learning rate of 0.001 for 15,000 steps, followed by 0.0001 for 3,000 steps. This staged approach allows for rapid initial convergence and fine-tuning.
• Importance of Learning Rate: A high learning rate speeds up convergence but can overshoot ideal values. A lower rate provides better final results through smaller adjustments.

Setup and Dependencies

• TensorFlow Installation: A specific TensorFlow version is installed to ensure compatibility with training scripts.
• Repository Cloning: The TensorFlow GitHub repository is cloned to access necessary scripts.

Monitoring with TensorBoard

• Functionality: TensorBoard provides a user interface to visualize training progress through graphs showing accuracy and cross-entropy loss.
• Training Visualization: Graphs update in real-time, displaying performance on training and validation datasets. Smoothing features help clarify trends.

Training Execution

• Command-Line Arguments: The training script is executed with various arguments to define model architecture, data directories, and training configurations.
• Dataset Download: The Speech Commands dataset is downloaded for training.

Handling Colab Runtime

• Runtime Management: Colab may disconnect if inactive for 90 minutes or after 12 hours. Regular interaction is necessary to prevent data loss.
• Reconnection: Users can reconnect to the runtime to continue monitoring training without losing progress.

Model Freezing and Conversion

• Checkpointing: Weights and biases are saved at checkpoints during training. If interrupted, training can resume from the last checkpoint.
• Freezing the Model: The model is frozen into a static graph with weights embedded, creating a file for inference.
• Conversion to TensorFlow Lite: The frozen model is converted to TensorFlow Lite format using toco, enabling deployment on microcontrollers.

Deployment Preparation

• Model Size: The final model is compact, suitable for deployment on hardware.
• C Array Conversion: The TensorFlow Lite model is converted into a C array for integration into projects.

Project Integration

• Model Replacement: Update the micro_features/tiny_conv_micro_features_model_data.cc file with the new model data.
• Label Update: Adjust labels in micro_features/micro_model_settings.cc to match the new model’s output categories.

Conclusion

The training and deployment process for a wake-word detection model involves careful management of training parameters, dependencies, and runtime environments. By following these steps, a compact and efficient model can be prepared for deployment on microcontrollers, enabling real-time wake-word detection in various applications.

Summary of Wake-Word Detection and Model Training

This text provides a guide for updating command responder code for different devices to recognize voice commands “on” and “off,” and explains how to train a machine learning model for wake-word detection.

Command Responder Updates

1. Arduino:

   • The command responder in arduino/command_responder.cc initially lights an LED for “yes.”
   • Update the code to light the LED for “on” or “off” by checking the first and second letters of the command: cpp if (found_command[0] 'o' && found_command[1] ‘n’) { … }

2. SparkFun Edge:

   • Located in sparkfun_edge/command_responder.cc, it lights different LEDs for “yes” or “no.”
   • Modify to light the yellow LED for “on” and the red LED for “off”: cpp if (found_command[0] 'o' && found_command[1] ‘n’) { … }

3. STM32F746G:

   • The command responder in disco_f746ng/command_responder.cc displays words on an LCD.
   • Update to display “Heard on!” or “Heard off” using similar logic: cpp if (found_command[0] 'o' && found_command[1] ‘n’) { … }

Training the Model

• Environment Setup:

  • Use a cloud VM with a GPU or a Linux workstation for efficient training.
  • Install TensorFlow nightly builds for model training: bash pip uninstall -y tensorflow tensorflow_estimator pip install -q tf-estimator-nightly1.14.0.dev2019072901 tf-nightly-gpu1.15.0.dev20190729

• Training Process:

  • Clone the TensorFlow repository and checkout a specific commit: bash git clone -q https://github.com/tensorflow/tensorflow git -c advice.detachedHead=false -C tensorflow checkout 17ce384df70

  • Train the model with specified parameters: bash python tensorflow/tensorflow/examples/speech_commands/train.py —model_architecture=tiny_conv —wanted_words=“on,off” …

• Model Conversion:

  • Freeze the trained model and convert it to TensorFlow Lite format: bash python tensorflow/tensorflow/examples/speech_commands/freeze.py … toco —graph_def_file=/tmp/tiny_conv.pb —output_file=/tmp/tiny_conv.tflite …

• Embedded System Integration:

  • Convert the TensorFlow Lite model into a C source file for embedded systems: bash xxd -i /tmp/tiny_conv.tflite > /tmp/tiny_conv_micro_features_model_data.cc

Understanding the Model

• Feature Generation:

  • Convert raw audio data to a spectrogram using feature generation techniques like Fourier transform and mel-frequency scaling.
  • Noise reduction and amplitude normalization are applied to enhance the signal.

• Model Architecture:

  • The model uses a convolutional layer to identify patterns, followed by a fully connected and softmax layer.
  • Filters in the convolutional layer detect specific patterns in the input spectrograms to differentiate commands.

This text provides a comprehensive overview of modifying command responders and training a wake-word detection model using TensorFlow, along with insights into the model’s architecture and feature generation process.

Summary

The text discusses the process of training a wake-word detection model using convolutional neural networks (CNNs) and fully connected layers. The model begins with a CNN that processes an input spectrogram by applying filters with a stride of two, reducing the image size by half. The input image, initially 49x40 pixels with a single channel, results in an output of 25x20 pixels with eight channels due to eight filters. This is followed by a fully connected layer, which performs a global pattern match using weights for each input value, producing four output values corresponding to different classes: “silence,” “unknown,” “yes,” and “no.”

The final layer is a softmax, which amplifies the difference between the highest output and its competitors, providing a score that represents the model’s confidence. However, these scores require calibration to be interpreted as probabilities. The model’s output is based on analyzing a one-second audio window, necessitating frequent runs (10-15 times per second) to capture entire words accurately. A postprocessing class averages scores over time and triggers recognition upon consistent high scores.

The text also covers training with custom data using the Speech Commands dataset, which includes over 100,000 one-second WAV files of various command words. The training script allows for customization by specifying desired words and adjusting parameters like silence and unknown sample percentages. The dataset and script enable training a model to recognize specific words by organizing audio files in a structured directory.

For custom datasets, users can specify their data directory and desired classes, which can include any audio event. The challenge lies in gathering sufficient data, as training requires thousands of examples covering real-world variations. Data augmentation techniques, such as altering volume and adding noise, help expand datasets but must be applied judiciously to avoid distorting inputs.

The text emphasizes the importance of the unknown category, which helps the model distinguish untrained words from command words. It also discusses the potential of transfer learning, a method that allows fine-tuning pre-trained models on new data, although it’s still under research for speech models.

Recording custom audio involves using tools like the Open Speech Recording app, which facilitates capturing and labeling utterances. The app stores recordings as OGG files, which need conversion to WAV format for training. The text provides instructions for handling and processing these files, including trimming recordings to ensure words are centered.

Finally, the text touches on model architecture, noting that the default model is small and fast but sacrifices accuracy. Users can modify the model architecture to suit their needs, balancing size, speed, and accuracy based on application requirements.

Recognizing spoken words with minimal memory is complex and involves feature generation, model architecture choices, data augmentation, and selecting suitable training data. The next chapter explores inference with more complex data, focusing on person detection using vision models. Vision is crucial for humans, and recent advancements in convolutional neural networks (CNNs) enable machines to interpret visual data. CNNs, inspired by the mammalian visual cortex, filter complex inputs into recognizable patterns and shapes.

Vision models are now used in various applications, from autonomous vehicles spotting road hazards to smartphones identifying faces. However, privacy concerns arise with internet-connected cameras. TinyML addresses these issues by enabling devices to process visual data locally without internet connectivity, thus maintaining privacy. For instance, a smart stove could detect unattended burners using a microcontroller with a camera, offering benefits without privacy trade-offs. These devices can operate for extended periods on small batteries, making them suitable for remote monitoring of endangered species.

The chapter focuses on building an embedded application using a pretrained person-detection model on a microcontroller with a camera. The application identifies the presence of a person in the camera’s view, lighting an LED as an example output. The source code is available in the TensorFlow GitHub repository, with deployment instructions for platforms like Arduino Nano 33 BLE Sense and SparkFun Edge.

Camera modules, essential for capturing image data, combine image sensors, lenses, and control electronics. The application follows a sequence: obtaining input, preprocessing for model compatibility, running inference, and postprocessing output. The person-detection model, a convolutional neural network, is trained on the Visual Wake Words dataset, consisting of 115,000 images labeled for person presence. The model, 250 KB in size, accepts 96x96 pixel grayscale images and outputs probabilities for person detection.

The application architecture involves capturing image data, running inference using TensorFlow Lite, and responding to detection results. The code includes a main loop, image provider, TensorFlow Lite interpreter, model, and detection responder. Tests ensure components function correctly, with person_detection_test.cc demonstrating inference on image data. The input tensor is a 5D tensor, with dimensions representing image rows, columns, and color channels. The model’s output, a 4D tensor, provides probabilities for person presence.

This chapter introduces the structure and components of a person detection application, offering insights into working with camera data on microcontrollers and running inference with vision models. The simplicity of handling image data compared to audio is highlighted, along with the potential for customizing applications to perform various tasks based on detection results.

The text describes a machine learning model designed to classify images into three categories: “unused,” “person,” and “not a person.” The model’s output tensor has redundant dimensions, but this doesn’t affect accessing the data, which is stored as a flat array. The model’s performance is evaluated by comparing the scores for “person” and “not a person” categories. If scores are similar, the result may be inconclusive.

The process involves feeding images into the model, running inference, and checking the scores. Tests are run using TensorFlow’s command line tools, and the image provider interface captures images in a format suitable for the model’s input tensor. The image provider’s implementation is platform-specific, with a reference implementation returning dummy data.

The detection responder communicates inference results, logging scores for “person” and “not a person” categories. The main application functions are in main_functions.cc, where the model is set up, and inference is run in a loop. The image provider captures images, and the detection responder processes the results.

Deploying to microcontrollers like Arduino Nano 33 BLE Sense and SparkFun Edge involves using a camera module, such as the Arducam Mini 2MP Plus. The Arducam captures images, which are resized and decoded from JPEG format to grayscale for model input. The Arduino implementation uses the camera to capture images and the board’s RGB LED to indicate detection results, with green for “person” and red for “not a person.”

The document provides a comprehensive walkthrough of setting up and running the model, capturing images, and responding to detections, making it a useful template for vision-based ML applications.

Summary

This text provides detailed instructions on building a person detection application using the Arduino Nano 33 BLE Sense and an Arducam Mini 2MP Plus. The focus is on configuring the hardware and software to detect the presence of a person using a camera module and the TensorFlow Lite library.

Hardware and Software Requirements

• Hardware: Arduino Nano 33 BLE Sense board, Arducam Mini 2MP Plus, jumper cables, micro-USB cable, and optionally a breadboard.
• Software: Arduino IDE and necessary libraries including Arduino_TensorFlowLite, Arducam, and JPEGDecoder.

Setup and Configuration

1. Connecting the Hardware: Use jumper cables to connect the Arducam to the Arduino board. Specific pin connections are detailed in Table 9-1.

2. Installing Libraries:

   • Use the Arduino IDE to install the TensorFlow Lite library. If issues arise, version 1.14-ALPHA is recommended.
   • For the Arducam library, download from GitHub, and configure it by editing memorysaver.h to define OV2640_MINI_2MP_PLUS.
   • Install JPEGDecoder via the Arduino IDE and configure User_Config.h to comment out SD library definitions.

Running the Application

• Deploying the Code: Load the person_detection example from the Arduino IDE, select the correct board and port, and upload the code.
• Testing: Point the camera at non-person objects and then at a person. The blue LED flashes during image capture, and inference results in either the red or green LED lighting up, indicating detection status.

Additional Features and Troubleshooting

• Serial Monitor: Use the Arduino Serial Monitor to view detailed logs and timestamps of the process.
• Environment Considerations: Ensure good lighting and correct camera orientation for accurate results. The model is small and may not always be 100% accurate.

Modifications and Extensions

• Experiment with the code to handle ambiguous inputs, control additional components, or build applications like a smart security camera.

SparkFun Edge Integration

For low-power applications, the SparkFun Edge board with a Himax HM01B0 camera module is recommended. This setup is optimized for battery-powered vision applications.

• Camera Initialization: Use the GetImage() function to initialize and capture frames, leveraging the Ambiq Apollo3 SDK and HM01B0 driver.
• Detection Response: Implement LED indicators for detection results, similar to the Arduino setup.

Building and Deploying on SparkFun Edge

• Requirements: SparkFun Edge board, Himax HM01B0 camera, USB programmer, and Python 3.
• Building the Binary: Follow the instructions in README.md to clone the TensorFlow repository, build the binary, and prepare it for deployment.

This comprehensive guide provides a clear pathway to implementing a person detection system on microcontroller platforms, highlighting the integration of hardware and software components to achieve efficient and effective results.

Summary

This guide outlines the process of deploying a person detection application to the SparkFun Edge microcontroller using TensorFlow Lite for Microcontrollers. The steps include compiling the binary, signing it, and flashing it to the device. The process involves several technical steps, each crucial for successful deployment.

Compiling the Binary

1. Compile the Binary: Use the Makefile to compile a binary for the SparkFun Edge.

   • Command: make -f tensorflow/lite/micro/tools/make/Makefile TARGET=sparkfun_edge person_detection_bin
   • Check binary existence: test -f tensorflow/lite/micro/tools/make/gen/sparkfun_edge_cortex-m4/bin/person_detection.bin

2. Sign the Binary: Use dummy cryptographic keys from the Ambiq SDK for development purposes.

   • Command to set up keys: bash cp tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info0.py tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/keys_info.py

   • Create a signed binary: bash python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_image_blob.py —bin tensorflow/lite/micro/tools/make/gen/sparkfun_edge_cortex-m4/bin/person_detection.bin —load-address 0xC000 —magic-num 0xCB -o main_nonsecure_ota —version 0x0

   • Finalize the binary for flashing: bash python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/create_cust_wireupdate_blob.py —load-address 0x20000 —bin main_nonsecure_ota.bin -i 6 -o main_nonsecure_wire —options 0x1

Flashing the Binary

1. Prepare the Device: Attach the SparkFun USB-C Serial Basic to the SparkFun Edge.

   • Align pins labeled BLK and GRN correctly.

2. Connect to Computer: Identify the device name by listing all connected devices before and after attaching.

3. Flash the Board: Put the board in bootloader state and run the flashing script.

   • Set environment variables: bash export DEVICENAME=<device_name> export BAUD_RATE=921600

   • Flash the binary: bash python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/uart_wired_update.py -b $BAU{D}_{R}ATE${DEVICENAME} -r 1 -f main_nonsecure_wire.bin -i 6

   • Reset the board using buttons marked 14 and RST.

Testing and Debugging

• Test the Program: The blue LED toggles for each inference cycle (around 6 seconds).
  • Orange LED indicates no person detected; green LED indicates person detected.
• Debugging:
  • Ensure button 14 is held during flashing.
  • Check connections if LEDs do not light up.
  • Verify camera module connection if red LED illuminates.

Viewing Debug Data

• Monitor the board’s serial port using: bash screen ${DEVICENAME} 115200

Modifications and Enhancements

• Modify detection responder logic.
• Use detection results to control other components.
• Develop a smart security camera application.

Conclusion

The process demonstrates the use of machine learning models for person detection, emphasizing the importance of pretrained models in development. The next steps involve understanding model training and creating custom vision models.

Setting Up JupyterLab and Google Cloud Platform

To begin training a person detection model, set up a Google Cloud Platform instance and open JupyterLab. Create a Python 3 notebook to run commands. Jupyter provides a web interface for executing Python code, storing commands, and results in a shareable notebook.

Training Framework and Model Preparation

Keras is the recommended interface for TensorFlow, but due to feature limitations, the tutorial uses tf.slim, an older, deprecated interface. Download the TensorFlow models repository from GitHub to access Slim. Ensure Python can find Slim modules and install dependencies like contextlib2.

Building the Dataset

The COCO dataset is used for training, containing images with bounding boxes for object localization, including people. Use Slim’s script to download and convert this data into labels. This process requires significant storage and time, resulting in a set of TFRecords for training.

Training the Model

Utilize tf.slim to train the model with customizable parameters via command-line arguments. The script train_image_classifier.py is used with specific settings like mobilenet_v1_025 for architecture and preprocessing. Training involves setting learning rates, batch sizes, and other parameters, requiring experimentation to optimize accuracy.

Monitoring Training with TensorBoard

TensorBoard provides visualizations of training progress. Start a session to view graphs of metrics like loss and accuracy. The clone_loss graph is crucial for assessing model convergence. The IMAGES tab shows preprocessed training images, offering insights into data handling.

Evaluating the Model

Evaluate model accuracy using checkpoints from the training directory. The script eval_image_classifier.py calculates accuracy, indicating the proportion of correctly classified images. A fully trained model should achieve around 84% accuracy with a loss of approximately 0.4.

Exporting the Model to TensorFlow Lite

Convert the trained model to a format suitable for embedded devices. First, export the model architecture using export_inference_graph.py. Then, freeze the weights using TensorFlow’s freezing script. Finally, quantize the model to reduce size and convert it to TensorFlow Lite format using the TFLiteConverter.

Converting to a C Source File

Since many embedded devices lack a filesystem, convert the TensorFlow Lite model into a C data array using xxd. This allows the model to be compiled into an executable and stored in flash memory for deployment on embedded systems.

Summary

This text covers the process of training and deploying machine learning models for image and gesture recognition on embedded devices. It includes a detailed explanation of the MobileNet architecture, a practical guide to customizing models using the COCO dataset, and an introduction to building a gesture-detecting application.

Training Models for Embedded Devices

• Model Customization: The COCO dataset contains over 60 object types, allowing for model customization by selecting different categories, such as cars instead of people. The process involves substituting paths in the dataset preparation scripts.

• Transfer Learning: If the desired object isn’t in COCO, transfer learning can be used with smaller custom datasets. This approach is still under exploration.

MobileNet Architecture

• Design: MobileNets are designed for efficiency with minimal weight parameters and operations. The v1 version is used for its low RAM requirements.

• Depthwise Separable Convolution: This technique breaks down convolution calculations into smaller parts, reducing computational load while maintaining accuracy. MobileNet v1 consists of 14 layers of these convolutions, with a width multiplier reducing computations significantly.

• Layer Functionality: Early layers detect low-level structures like edges, while later layers synthesize information for object classification.

Image Recognition Challenges

• Data and Processing: Image recognition requires large datasets and significant processing power. The text provides a foundation for training models and optimizing them for embedded systems.

Building a Gesture-Detecting Application

• Complex Data: Machines generate data that is often difficult for humans to interpret. The application uses accelerometer data to detect gestures, transforming complex sensor data into understandable outputs.

• Heuristics vs. Machine Learning: Traditional heuristics require domain knowledge and programming expertise, while machine learning models can learn from labeled data, reducing the need for deep domain knowledge.

• Application Example: A digital “magic wand” uses accelerometer data to detect gestures (“wing,” “ring,” “slope”) and performs actions like lighting an LED. This demonstrates how deep learning can provide insights into complex data.

Application Architecture

• Components: The application includes a main loop, accelerometer handler, TF Lite interpreter, model, gesture predictor, and output handler. It processes raw accelerometer data to classify gestures.

• Model Details: The model is a compact convolutional neural network, trained on four gestures, and outputs probability scores for classification.

• Inference Process: The application runs multiple inferences per second, requiring confirmation of gestures through repeated detections to ensure accuracy.

Deployment and Testing

• Supported Platforms: Instructions are provided for deploying on Arduino Nano 33 BLE Sense and SparkFun Edge. The text outlines the application’s structure and testing process, demonstrating the end-to-end inference workflow.

This comprehensive guide highlights the potential of machine learning on embedded devices, offering practical insights into model training, deployment, and application development.

TensorFlow Lite Micro Inference Process

Model Setup and Input Handling

1. Tensor Allocation: Allocate tensors for model inference. Obtain a pointer to the input tensor and verify its shape and type. The input tensor should have dimensions (1, 128, 3, 1) with 32-bit floating-point values representing accelerometer readings.

2. Data Input: Load input data from a predefined source (g_circle_micro_f9643d42_nohash_4_data), representing a gesture, into the input tensor.

Inference Execution

1. Model Invocation: Run the model using the Invoke() method. Check the invocation status to ensure it executes successfully.

2. Output Verification: Obtain a pointer to the output tensor and verify its shape. The expected output is a set of four probabilities indicating possible gestures.

Output Analysis

1. Score Evaluation: Compare scores for different gestures (e.g., “ring”, “slope”) to determine the highest probability, indicating the detected gesture.

2. Repeat Process: Test with different input data (e.g., “slope” gesture) and ensure the model correctly identifies the gesture by comparing output scores.

Accelerometer Handler

1. Setup: Initialize the accelerometer handler, which populates the input tensor with accelerometer data.

2. Data Reading: Use ReadAccelerometer() to fill the input tensor with data. The function returns a boolean indicating if sufficient data is available for inference.

Gesture Prediction

1. PredictGesture Function: Analyze output tensor probabilities to detect gestures. A gesture is recognized if it meets a minimum probability threshold and is consistently detected over multiple inferences.

2. Thresholds: Use device-specific thresholds for consecutive detections, ensuring reliable gesture recognition.

Testing and Execution

1. Gesture Predictor Tests: Validate the PredictGesture() function with scenarios like successful predictions, interrupted sequences, and insufficient probabilities.

2. Output Handling: The output handler displays results based on the gesture prediction index.

Main Function

1. Setup Function: Initialize variables, load the model, and set up the interpreter for inference.

2. Loop Function: Continuously read data from the accelerometer and run inference. Use results to update the output handler.

This setup allows for real-time gesture recognition using accelerometer data, leveraging TensorFlow Lite Micro for efficient on-device inference.

The text outlines the implementation of a gesture detection application using TensorFlow Lite for Microcontrollers, specifically focusing on deploying to devices like the Arduino Nano 33 BLE Sense and SparkFun Edge. The process includes reading accelerometer data, running inference, and handling outputs.

Key Components

1. Gesture Detection Loop:

   • The loop function reads accelerometer data. If new data is available, inference is run using the TensorFlow Lite interpreter.
   • The PredictGesture() function analyzes the inference results to determine the detected gesture index.
   • If the gesture index is valid (less than 3), the buffer is cleared for the next reading, and results are reported using HandleOutput().

2. Program Execution:

   • The main() function initializes the setup and continuously calls the loop function.
   • The program is built and run using specific make commands, but it requires accelerometer data to produce output.

3. Deployment to Microcontrollers:

   • The guide provides steps for deploying the application on Arduino Nano 33 BLE Sense and SparkFun Edge.

4. Arduino Implementation:

   • The Arduino Nano 33 BLE Sense is equipped with a three-axis accelerometer, making it suitable for gesture detection.
   • Constants and thresholds specific to Arduino are defined to ensure accurate gesture detection.

5. Accelerometer Data Handling:

   • The accelerometer handler captures data and writes it to the model’s input buffer.
   • Data from the Arduino accelerometer, sampled at 119 Hz, needs downsampling to 25 Hz to match the model’s requirements.
   • Downsampling is done by retaining one in every five samples.

6. Setup and Data Capture:

   • SetupAccelerometer() prepares the board for data capture by initializing the IMU.
   • ReadAccelerometer() captures new data, clears the buffer if required, and writes data to the input tensor for inference.

7. Output Handling:

   • The output handler toggles the board’s LED and logs detected gestures to the serial port.
   • ASCII art is displayed based on the detected gesture type.

8. Running the Example:

   • Instructions for deploying the example on an Arduino Nano 33 BLE Sense using the Arduino IDE are provided.
   • The TensorFlowLite library must be installed, and specific modifications are needed for the Arduino_LSM9DS1 library to enable the FIFO buffer.

This summary captures the essential steps and considerations for implementing and deploying a gesture detection application on microcontroller platforms using TensorFlow Lite. The focus is on ensuring compatibility with the hardware and achieving accurate gesture recognition through data processing and inference.

Summary

This guide provides instructions on deploying a gesture-recognition application on an Arduino device and a SparkFun Edge board using accelerometer data. The process involves setting up the hardware, configuring the software, and troubleshooting common issues.

Arduino Setup

1. Device Configuration:

   • Connect the Arduino via USB.
   • Select the correct board type and port in the Arduino IDE.
   • Install necessary support packages if the device is not listed.

2. Uploading Code:

   • Click the upload button in the Arduino IDE to compile and transfer the code.
   • After uploading, the Arduino’s LED should flash, indicating readiness for gesture recognition.

3. Gesture Testing:

   • Hold the board with components facing up and USB adapter to the left.
   • Perform gestures like “wing,” “ring,” and “slope” as shown in diagrams.
   • Successful gestures will illuminate the red LED and display messages.

4. Troubleshooting:

   • If the LED doesn’t light, try resetting or reconnecting the device.
   • Ensure the board is held correctly and gestures are performed smoothly.

5. Customizing the Application:

   • Modify threshold values in the code to adjust gesture sensitivity.
   • Extend functionality using Bluetooth for wireless operation.

SparkFun Edge Setup

1. Hardware Requirements:

   • SparkFun Edge board, USB programmer, and cables.
   • Python 3 for building the binary.

2. Accelerometer Configuration:

   • The SetupAccelerometer() function configures the accelerometer.
   • The FIFO buffer on the accelerometer collects data for inference.

3. Data Handling:

   • ReadAccelerometer() function retrieves data from the FIFO buffer.
   • Data is processed and stored for gesture prediction.

4. Output Handling:

   • The output handler configures LEDs to indicate gesture detection.
   • Different LEDs light up for different gestures, with corresponding ASCII art output.

5. Building and Deploying:

   • Clone the TensorFlow repository and build the binary using provided commands.
   • Sign the binary with cryptographic keys for deployment.

6. Flashing the Device:

   • Generate a signed binary and flash it to the SparkFun Edge using the provided scripts.

Additional Notes

• Bluetooth Support: Examples for Bluetooth integration are available in the Ambiq SDK and ArduinoBLE library.
• Gesture Practice: Watching demonstration videos can help in mastering the gestures.
• Code Modification: Users are encouraged to experiment with code changes to enhance functionality.

This guide ensures a smooth setup for deploying gesture-based applications on microcontroller platforms, providing a basis for further experimentation and development.

Flashing and Programming the SparkFun Edge

To update the SparkFun Edge microcontroller with a new program, you’ll need to flash the device’s flash memory using a binary file. This process overwrites any existing program. The SparkFun USB-C Serial Basic programmer facilitates communication between your computer and the microcontroller via USB.

Steps to Flash the Device:

1. Attach the Programmer:

   • Locate the six-pin header on the SparkFun Edge.
   • Connect the SparkFun USB-C Serial Basic to these pins, aligning the BLK and GRN labels.

2. Connect to Computer:

   • Attach the board to your computer via USB.
   • Identify the device name by listing all connected devices using:
     • macOS: ls /dev/cu*
     • Linux: ls /dev/tty*
   • The new item in the list after connection is the device name, typically starting with “wch”.

3. Prepare for Flashing:

   • Set the device name in a shell variable: export DEVICENAME=<your device name>
   • Set the baud rate: export BAUD_RATE=921600

4. Flash the Binary:

   • Use the following command to flash: bash python3 tensorflow/lite/micro/tools/make/downloads/AmbiqSuite-Rel2.0.0/tools/apollo3_scripts/uart_wired_update.py -b $BAU{D}_{R}ATE${DEVICENAME} -r 1 -f main_nonsecure_wire.bin -i 6

   • Reset the board by holding the button marked 14, pressing RST, then running the script.

5. Verify Flashing:

   • Successful flashing will show “Sending Reset Command. Done.” If errors occur, ensure the RST command was printed.

Testing the Program:

• Press the RST button to start the program. The yellow LED should toggle on/off for each inference.
• Use the command screen ${DEVICENAME} 115200 to view serial output.
• Perform gestures like “wing”, “ring”, and “slope” to test functionality. Success is indicated by the red LED lighting up.

Troubleshooting:

• Flashing Errors: Ensure button 14 is held during the script run.
• LED Issues: Press RST or reconnect the board if LEDs don’t behave as expected.
• Gesture Recognition: Ensure correct board orientation and practice gestures. Start with the “wing” gesture for ease.

Modifying the Application:

• Explore and modify the code in the tensorflow/lite/micro/examples/magic_wand folder.
• Experiment with threshold values in constants.cc to adjust gesture sensitivity.
• Consider extending functionality, such as transmitting results via Bluetooth.

Conclusion

This guide demonstrates how to interpret sensor data using embedded machine learning to recognize gestures with the SparkFun Edge. By understanding and modifying the model, you can further customize the application for various uses.

The process of preparing and training a machine learning model involves several key steps, focusing on data preparation, splitting, augmentation, model training, and evaluation.

Data Preparation

The data_prepare.py script cleans raw data by removing garbage characters and generating synthetic data. This synthetic data, created algorithmically, helps train the “unknown” category, which is crucial for model robustness. However, synthetic data cannot replace real-world data due to its unpredictable nature.

Data Splitting

The data_split_person.py script divides data into training, validation, and test sets based on the person who created it. This approach ensures the model’s ability to generalize across different individuals. The data is split into approximately 60% for training, 20% for validation, and 20% for testing. Splitting by person helps test the model’s generalization capabilities, unlike random splitting, which may not accurately reflect the model’s performance on unseen data.

Training Process

The training process begins with loading TensorBoard for monitoring. The train.py script sets up the model architecture using a Convolutional Neural Network (CNN), loads data, and performs data augmentation. Augmentation involves modifying data slightly to enhance the dataset’s diversity, crucial for effective training.

The model’s architecture includes layers such as Conv2D, MaxPooling2D, Dropout, Flatten, and Dense layers. These layers work together to process input data and extract features. The model’s input shape is (None, 128, 3), and it is trained to classify gestures into predefined categories.

Model Evaluation

Post-training, the model’s performance is evaluated using validation and test datasets. Validation accuracy reached 97.43%, indicating strong performance. The test dataset evaluation yielded an accuracy of 93.23%, demonstrating the model’s reliability. A confusion matrix helps identify misclassifications, suggesting areas for improvement, such as obtaining more training data for certain gestures.

Model Conversion and Deployment

The trained model is converted to TensorFlow Lite format, significantly reducing its size, which is beneficial for deployment in resource-constrained environments. The model can be further transformed into a C source file for integration into applications.

Understanding the Model Architecture

The CNN model processes a sequence of 128 three-axis accelerometer readings to predict gestures. Convolutional layers extract features by sliding a window across the data, identifying patterns. Filters in these layers learn to recognize specific features, progressively transforming raw data into a high-level symbolic representation.

Overall, the training process emphasizes the importance of data preparation, augmentation, and careful evaluation to develop a robust model capable of generalizing across different users and scenarios.

The text describes a convolutional neural network (CNN) model used for classifying time-series sensor data, specifically accelerometer data for gesture recognition. The model architecture includes several key components:

1. Convolutional Layers: The model begins with convolutional layers that apply filters to the input data, creating feature maps. Padding is used to ensure the filter covers the entire data set. These layers identify patterns in the data and merge information from the three accelerometer axes into single values.

2. Max Pooling: After convolution, a MaxPool2D layer reduces the size of the data by selecting the maximum value in each window of input data, concentrating the most relevant information and discarding less significant details. This helps transform a complex input into a simpler output.

3. Dropout: The model employs Dropout layers, which randomly set a percentage of the tensor’s values to zero during training. This regularization technique prevents overfitting by forcing the network to handle variability and noise.

4. Dense Layers: The data is flattened and fed into Dense layers, where each input is connected to every neuron. This allows the model to learn the meanings of various input combinations. The final Dense layer uses a softmax activation function to output probabilities for each gesture class.

5. Training Process: The text outlines how to train the model with custom data. This involves capturing accelerometer data, modifying training scripts, and experimenting with hyperparameters to improve accuracy. The process includes data collection, script modification, and model training.

6. Implementation on Devices: The trained model is implemented on devices like the SparkFun Edge, where modifications to the code are necessary to handle new gestures. This includes updating model data, adjusting prediction counts, and ensuring correct gesture names are printed.

7. Embedded Machine Learning: The model architecture is suitable for embedded machine learning applications, providing a compact and efficient tool for gesture recognition. The text emphasizes the importance of understanding model architecture and training processes for developing effective embedded systems.

8. Further Learning: The text suggests resources for deeper learning in machine learning, including books and online courses, to expand knowledge beyond the basics covered.

In summary, the text provides a detailed overview of CNN architecture for gesture recognition, the training process for custom data, and the implementation of models on embedded devices, highlighting the model’s efficiency and adaptability for real-world applications.

TensorFlow is Google’s open-source machine learning library, first released in 2015. It supports Linux, Windows, and macOS, providing tools for training and deploying models in the cloud. The main interface language is Python, and the library is widely used within Google and by external contributors. TensorFlow Lite, launched in 2017, addresses the need for a smaller framework for mobile devices by removing less common features and focusing on inference rather than training. It supports 8-bit quantization, reducing model size and increasing inference speed.

TensorFlow Lite for Microcontrollers, introduced in 2018, further reduces size constraints for embedded platforms, aiming to fit within 20 KB. It avoids dependencies on operating systems, standard C/C++ libraries, and dynamic memory allocation. Instead, it uses a fixed-size memory arena for temporary allocations. The library is written in C++11 and targets 32-bit processors, maintaining compatibility with mobile TensorFlow Lite.

Project generation in TensorFlow Lite offers a middle ground between code generation and model interpretation. It allows for easy integration into build systems, modifiability, and the ability to handle multiple models without recompilation. The interpreter structure, combined with the OpResolver mechanism, helps manage code size by registering only necessary kernel implementations.

TensorFlow Lite supports various build systems, including Make, Mbed, Keil, and Arduino, through project generation. This process generates self-contained folders with all dependencies and project files, facilitating cross-platform compatibility. Continuous integration workflows ensure updated project files are available online, making them accessible without needing to use GitHub.

Overall, TensorFlow Lite and its microcontroller variant provide scalable solutions for deploying machine learning models across diverse platforms, balancing size constraints with functionality and ease of use.

Summary of TensorFlow Lite for Microcontrollers Implementation

Modular Approach

The library is divided into small modules, each with a default C++ implementation and a header defining the interface. To specialize a module for a specific platform, a new implementation file is saved in a subfolder named after the target platform. This structure allows automatic selection of the specialized implementation during the build process.

Audio Provider Example

The default audio provider implementation returns zero-filled buffers, allowing cross-platform compilation without a microphone. The interface includes GetAudioSamples and LatestAudioTimestamp. Specialized implementations, such as for the STM32F746NG board, replace the default to capture real audio data. The build system selects the specialized file based on the platform specified, such as disco_f746ng/audio_provider.cc.

Optimization Mechanism

The library supports optimizations through specialized implementations. The depthwise convolution operation, critical for the speech wake-word model, is optimized in portable_optimized/depthwise_conv.cc. This file includes enhancements like loading values as 32-bit words to improve performance. These optimizations are platform-independent, unlike the platform-specific audio provider example.

Build System with Makefiles

Make is used for its flexibility, despite its complexity. It supports project generation for modern IDEs. The build system uses a custom specialize() function to handle platform-specific source substitutions. Makefiles automatically include new C++ files from standard folders using wildcard rules.

Platform Customizations

Platform-specific customizations are managed in sub-Makefiles. These files define compiler paths, architectures, flags, and additional libraries. For example, the apollo3evb_makefile.inc includes settings for the Ambiq and SparkFun Edge platforms.

External Libraries and Project Generation

External libraries are downloaded using the add_third_party_download rule. The build system ensures all necessary files, including licenses, are included. The generate_microlite_projects() function creates standalone projects for different IDEs, adapting files as needed for specific environments.

Testing Framework

Unit tests are required for new source files. Tests are organized as _test.cc files and must adhere to constraints suitable for microcontrollers, avoiding dynamic memory allocation and external dependencies. A minimal test framework is provided, using macros like TF_LITE_MICRO_TEST() for assertions.

This modular and flexible structure allows for efficient development, testing, and optimization across a wide range of platforms, ensuring that TensorFlow Lite for Microcontrollers can be adapted and enhanced for various use cases.

Summary of TensorFlow Lite for Microcontrollers

TensorFlow Lite for Microcontrollers aims to facilitate running machine learning models on various devices with different operating systems and architectures. The core code is designed for portability, and the build system simplifies the process of setting up new environments. This guide outlines steps to implement TensorFlow Lite on new hardware platforms, focusing on logging and debugging functionalities.

Testing Framework

The testing framework uses TF_LITE_MICRO_TESTS_BEGIN and TF_LITE_MICRO_TESTS_END to define test cases. Successful test execution outputs a specific string, ~~~ALL TESTS PASSED~~~, which can be used for automated test systems by scanning logs.

Logging Mechanism

Logging is crucial for debugging and verifying test success. On platforms like Linux, macOS, or Windows, logging can be achieved using standard C libraries. However, microcontrollers often lack these libraries, requiring platform-specific solutions. For example, Arm Cortex-M microcontrollers use semihosting to communicate with the host system. The DebugLog() function is essential for logging and must be adapted for each platform.

Implementing DebugLog()

1. Initial Setup: Start by creating a minimal example that prints a string to the host console using available logging facilities.
2. Modify DebugLog: Replace the default implementation in debug_log.cc with a platform-specific version if necessary.
3. Testing: Use the existing test for DebugLog() to verify functionality. The test involves reporting errors and confirming output.

Supporting New Platforms

1. Create a Subfolder: Use a unique identifier for your platform and create a subfolder in the source tree.
2. Integrate Changes: Copy your DebugLog() implementation into this folder and integrate it with the build system.
3. Build and Test: Use Makefile commands to generate projects and test binaries for your platform.

Advanced Integration

• Makefile Build: For broader integration and continuous integration testing, modify Makefiles to include platform-specific toolchains and dependencies.
• IDE Support: TensorFlow Lite supports generating standalone projects for various IDEs, but additional transformations may be necessary for some environments.

Code Management

To manage updates and changes efficiently:

1. Download or Generate New Project Files: Ensure folder structures match between new and existing project versions.
2. Merge Changes: Use a merge tool to incorporate updates from the new version into your modified project files.

By following these steps, developers can effectively port TensorFlow Lite for Microcontrollers to new platforms, ensuring robust testing and logging capabilities while maintaining code portability and manageability.

Summary

Merging and Version Control

Merging changes in projects using TensorFlow Lite for Microcontrollers can vary in complexity based on local modifications. Typically, updates are on the framework side, allowing developers to “accept theirs.” For minimal local changes, manual merging or copying modified code may suffice. Advanced users can leverage Git by checking modified code, importing new project files as a branch, and using Git’s merge facilities. Maintaining a single “source of truth” for development files is crucial, especially with multiple developers, to avoid incompatible changes. Using a source-control system with a single file copy is recommended. For migrating changes back to the main repository, track modified files and copy them into the Git repository at their original paths.

Contributing to Open Source

TensorFlow has more external contributors than internal, particularly for microcontroller work. Contributions via pull requests are encouraged, with a code review process managed through GitHub. Significant changes may require a design document, which can be discussed with SIG Micro. Maintaining a public fork allows for feedback on experimental changes. Automated tests run against pull requests, although they can be unreliable. Test coverage is aimed at 100%, and new code should follow the Google style guide, using clang-format for consistency.

Supporting New Hardware Accelerators

TensorFlow Lite for Microcontrollers serves as a reference platform for hardware developers, focusing on machine learning tasks like writing exporters and implementing operations. Developers should first run unoptimized reference code to ensure functionality before focusing on hardware optimization. The interpreter doesn’t support asynchronous execution, favoring synchronous coprocessors that work in lockstep with traditional processors. Quick prototyping is possible by replacing operator implementations at the kernel level with specialized hardware calls.

Understanding the File Format

TensorFlow Lite uses a complex model file format, but it becomes manageable with an understanding of the fundamentals. Models are graphs of operations with inputs and outputs, typically trained on desktops and converted for other devices using a converter. The conversion involves turning variable training values into constants and optimizing operations. The TensorFlow Lite file format provides a simpler, stable representation of models, recommended for accessing models for inference.

FlatBuffers

FlatBuffers is the serialization library used, suitable for embedded systems due to its performance. It allows models to be embedded directly into flash memory without parsing or copying. The library uses a schema to define data structures, generating native code for reading and writing. TensorFlow Lite’s schema defines a Model as the root, with properties like buffers for constant tensors. The FlatBuffers Vector class is a read-only wrapper, ensuring no dynamic memory allocation is required, making it efficient for embedded environments.

TensorFlow Lite for Microcontrollers: Key Concepts

TensorFlow Lite for Microcontrollers is designed to run machine learning models on embedded systems with limited resources. The framework uses a serialization format that includes tensors, operators, and subgraphs.

Tensors and Buffers

• Tensors: Store metadata about data such as shape and type. They reference constant buffers for values or use zero for dynamically calculated values.
• Buffers: Hold raw byte data without metadata. They are indexed in a top-level vector.

Subgraphs

• A subgraph is a collection of operators, connections, inputs, outputs, and buffers. Currently, only single subgraph models are supported for microcontrollers.
• Subgraphs have properties like tensors, inputs, outputs, and operators, which define their structure and data flow.

Operators

• Operators: Perform operations on tensors and are listed in topological order for execution efficiency.
• Each operator has an opcode_index referencing a list of valid operations and may use built-in or custom options for configuration.

Serialization Format

• The format includes elements like Operator, Tensor, and SubGraph, where each has specific roles and attributes.
• Operators may have parameters defined in the schema, like Conv2DOptions, or use FlexBuffers for custom operators.

Porting TensorFlow Lite Ops to Microcontrollers

Porting operations from TensorFlow Lite to microcontrollers involves several steps to adapt to the constraints of embedded systems.

Steps for Porting

1. Separate Reference Code: Extract necessary functions from reference_ops.h into smaller headers to reduce dependencies.
2. Create a Micro Copy of the Operator: Modify the mobile version for micro use, focusing on avoiding dynamic memory allocation and unnecessary parameter checks.
3. Port Tests to the Micro Framework: Adapt tests to use the Micro Test library, avoiding dynamic memory and C++ STL classes.
4. Build and Test: Use Bazel to compile and test the operator on x86 before cross-compiling for embedded devices using Makefiles.

Testing and Integration

• Bazel Tests: Ensure the operator works on desktop Linux.
• Makefile Tests: Compile and test on embedded targets, such as the SparkFun Edge.
• AllOpsResolver: Add the operator to the resolver to facilitate easy integration into applications.

Conclusion

The TensorFlow Lite for Microcontrollers framework is tailored for efficient execution on embedded systems by simplifying the model structure and adapting operations to the constraints of these environments. Porting involves careful code separation and testing to ensure compatibility and performance.

In the text, the focus is on porting TensorFlow Lite Mobile Ops to microcontrollers, specifically using a Cortex-M3 emulator. The emulator can be unreliable, but retrying may resolve issues. Contributors are encouraged to share their work with the open-source community, with guidance available in the TensorFlow Community guide.

The text transitions to designing TinyML applications, emphasizing the need for a thorough understanding of TensorFlow Lite for Microcontrollers. It acknowledges the complexity of the material and aims to provide foundational knowledge for deploying custom machine learning solutions. The book covers optimization, debugging, model porting, privacy, and security.

Designing TinyML applications involves adapting existing reference applications or building solutions from scratch when no clear starting point exists. The text stresses the importance of planning and experimentation to mitigate risks, as training models and setting up hardware can be time-consuming.

A crucial decision is whether to use a microcontroller or a larger device. Microcontrollers are advantageous for scaling due to their low cost, size, and energy consumption. However, initial prototyping with more accessible devices like Raspberry Pi or NVIDIA Jetson can be beneficial to refine requirements before committing to embedded systems.

Deep learning excels at tasks humans can solve quickly, such as recognizing objects and sounds. Neural networks are adept at pattern recognition but not higher-level tasks like planning. They can be components of larger systems, providing foundational insights.

Researching existing literature is recommended to understand applicable models and datasets. This helps in leveraging previous work and avoiding redundant efforts. For instance, using the Case Western Reserve University bearing dataset for predictive maintenance can expedite experimentation.

The text advises experimenting in a training environment without resource constraints to identify effective model architectures. Matching sensor data characteristics to similar tutorials, like using audio data for vibration analysis, can guide model selection.

Feature generation is crucial but often underexplored. It involves transforming raw data into inputs for neural networks. While modern vision tasks use raw RGB data, other sensors may require preprocessing, like converting audio samples into spectrograms.

Data exploration is emphasized, as understanding and improving datasets can yield better results than focusing solely on model architecture. Analyzing data for anomalies and ensuring balanced training sets are key steps.

Finally, the text introduces “Wizard of Oz-ing,” a technique that involves simulating functionalities to refine requirements before full implementation. This approach helps in determining the practical effectiveness of solutions without extensive technological investment.

Overall, the text provides a comprehensive guide to developing TinyML applications, highlighting the importance of planning, experimentation, and leveraging existing resources to achieve successful deployments.

Developing a machine learning model is time-consuming, and using the Wizard of Oz approach can help refine requirements before full-scale development. This involves creating a mock-up system where a person makes decisions instead of software, allowing you to test assumptions and generate labeled data for training. For example, in a project to detect room occupancy, you might use a person to manually control lights based on video feeds, identifying usability issues early.

Once requirements are validated, you should first get your model working on a desktop. This allows for quick experimentation and iteration, as exporting and running models on embedded platforms can be slow. By streaming sensor data to a desktop, you can evaluate your model’s performance in context, record data for repeated testing, and identify high-impact errors. Starting with platforms like Raspberry Pi, you can collect data, train models using TensorFlow, and evaluate them offline. Once a promising model is developed, convert it to TensorFlow Lite and test it in real environments via a simple web API.

Embedded systems have limited computing power, making latency optimization crucial. Slow processing can miss brief events or reduce prediction accuracy. Reducing latency can improve accuracy and energy efficiency by allowing devices to operate at lower CPU frequencies. First, determine if optimizing neural network code will significantly impact overall latency. If not, focus on other application parts. If necessary, consider hardware changes or creating a new model architecture that requires fewer calculations, trading accuracy for speed.

Model architecture improvements can significantly impact latency. Simplifying models can speed up inference without code changes. Experimenting with different architectures and removing layers can help. Estimating model latency often involves calculating FLOPs (floating-point operations), providing a rough metric for execution time. Comparing models based on FLOPs can guide expectations for hardware performance.

Quantization can reduce model size and speed up execution by converting 32-bit floating-point representations to 8-bit without significant accuracy loss. This takes advantage of deep learning models’ robustness to numerical precision loss. Post-training weight quantization reduces model size and offers speed benefits, while post-training integer quantization allows execution without floating-point calculations, ideal for embedded platforms. Quantization requires estimating activation layer ranges using example inputs during model export.

Overall, optimizing machine learning models for embedded systems involves validating assumptions early, using desktop environments for initial testing, and focusing on reducing latency through hardware and model architecture improvements. Quantization techniques further enhance performance, making models suitable for resource-constrained environments.

Summary

In optimizing machine learning models, especially for embedded systems, several strategies can enhance performance and reduce latency. A critical step is quantization, which involves converting models to use int8 data types for better efficiency. During this process, creating a representative dataset function is crucial as it provides the necessary inputs for accurate activation range estimation. This function requires careful consideration since input expectations vary with each model.

Optimizing latency can also be influenced by product design. Relaxing network requirements for speed or accuracy can lead to significant improvements. For example, using faster optical tracking algorithms for interim results or delegating complex tasks to cloud services can optimize performance. Incorporating uncertainty into user interfaces can also allow for a trade-off between accuracy and speed.

Code optimization is essential but should be approached after other strategies. TensorFlow Lite for Microcontrollers is designed for minimal binary footprint, but platform-specific optimizations can yield better results. Profiling is foundational to optimization, helping identify time-consuming code sections. Methods like using LEDs for rough timing, commenting out code sections (shotgun profiling), and using logic analyzers or timers can provide insights into code performance.

Operations within neural networks often consume the most time. Identifying and optimizing these operations is crucial. TensorFlow Lite provides default implementations focused on portability rather than speed, but faster versions exist in the kernels/portable_optimized directory. If necessary, custom implementations can be developed by focusing on specific input and output sizes.

Writing optimized code involves identifying critical sections, such as deeply nested loops, and moving work outside these areas. Parameter-specific optimizations can be implemented, but it’s important to ensure correctness through frequent testing. Leveraging platform-specific hardware features can further enhance performance, although this requires careful exploration of available documentation and resources.

Overall, optimizing machine learning models for embedded systems involves a combination of quantization, product design considerations, code profiling, and operation-specific enhancements. These strategies, when applied thoughtfully, can significantly improve model performance and reduce latency.

Summary

Optimizing code for embedded systems involves restructuring to minimize work, which simplifies further optimizations. On platforms like Cortex-M devices with SIMD instructions, it’s tempting to jump into using intrinsics or assembly for neural network inference. However, it’s advisable to first check vendor-supplied libraries for optimized solutions. Utilizing pre-existing functions for common calculations, like fast Fourier transforms, can save effort and improve performance.

For embedded systems, converting code to assembly can further optimize latency. Start by replacing individual lines with assembly equivalents to ensure correctness before focusing on speed improvements. Embedded systems are simpler than complex processors, making it feasible to predict performance and optimize at the assembly level without unexpected side effects.

As machine learning grows in embedded systems, specialized hardware accelerators are becoming more common. These accelerators are designed to work synchronously with the main processor, sharing memory space and blocking until results are returned. This model resembles early x86 floating-point coprocessors more than modern GPUs, which handle tasks asynchronously.

The synchronous model is preferred for low-energy systems, keeping runtime simpler. Coprocessor-like accelerators must be close to the CPU for low latency. In contrast, GPUs operate independently, queuing commands for batch processing, which suits graphics rendering and deep learning training.

Contributing to open-source projects like TensorFlow Lite involves sharing optimizations back to the community. This process starts by joining the SIG Micro mailing list, summarizing work, and providing benchmarks. Contributions should be maintainable and useful for future development.

The fastest code is none at all, so optimizing at the model and algorithm level is crucial before individual functions. Addressing latency is often necessary for application functionality, followed by energy optimization to ensure device longevity.

Embedded devices excel in low energy consumption compared to desktops or mobiles. A microcontroller can run on less than a milliwatt, suitable for long-term operation on limited power sources like coin batteries. Battery life is a significant constraint, influenced by energy use and storage capacity.

Understanding power usage requires intuition about energy demands of different operations. Power is energy over time, and average power usage directly affects device runtime. For example, a system using 1 mW will last twice as long as one using 2 mW.

Typical power usage varies: an Arm Cortex-A9 CPU uses 500-2,000 mW, while embedded components like microphones and accelerometers use much less. Radio communication is notably power-intensive. Energy storage and harvesting capabilities also vary, with CR2032 batteries offering about a month of power at 1 mW usage.

Choosing components involves reviewing datasheets for power usage, often found by searching for “milliamps.” Real-world testing of assembled systems is crucial, as integration can reveal higher consumption than expected. Measuring power with a known-capacity battery provides practical insights.

Estimating model power usage involves latency and average power calculations. Early estimates should be conservative, using them as upper bounds for performance expectations.

Summary

Optimizing energy usage in embedded systems involves several strategies, focusing primarily on software techniques that do not require extensive hardware knowledge. One common method is duty cycling, where the processor enters a low-power sleep mode between tasks, significantly reducing power consumption. Modern microcontrollers with direct memory access (DMA) capabilities can continue gathering sensor data even while the main processor is asleep. Additionally, reducing the clock frequency can also decrease energy use by trading computation speed for power efficiency.

Cascading design is another approach that leverages machine learning’s scalability. A small, power-efficient model runs continuously to detect potential events, triggering more complex models only when necessary. This method is similar to how always-on voice interfaces function, where a digital signal processor (DSP) monitors for wake words, waking the main CPU only when needed. This cascading approach ensures that more powerful systems operate minimally, conserving energy.

When optimizing for energy consumption, it is crucial to focus on latency first, ensuring the product functions correctly before addressing energy efficiency. This iterative process often involves balancing latency, energy, and space optimizations.

Memory and Storage Considerations

Embedded systems typically have limited storage and memory, often less than 1 MB of flash storage and 512 KB of SRAM. Understanding these constraints is vital for designing efficient applications. TensorFlow Lite for Microcontrollers is optimized for minimal resource usage, requiring as little as 20 KB of flash and 4 KB of SRAM, but careful engineering is necessary to maintain a low footprint.

To manage memory and storage effectively, consider the following:

• Flash Usage: Estimate the required flash memory by compiling a complete executable. Key factors include the operating system size, TensorFlow Lite code size, model data size, and application code size. Quantization can reduce model size by compressing weights, which are typically stored as floating-point values.

• RAM Usage: Estimating RAM needs is challenging due to variable usage over the program’s lifecycle. Consider the operating system size, TensorFlow Lite RAM size, model memory size, and application memory size. Proper planning and understanding of these components can prevent runtime errors and crashes.

Model Accuracy and Size

Understanding the limitations and capabilities of machine learning models on embedded systems is crucial. For instance, a small 18 KB model can achieve 85% accuracy in distinguishing between four sound classes. However, practical accuracy may be lower in real-world applications. Generally, more complex models with larger weights and operations are needed for higher accuracy, especially for tasks like wake word detection in voice interfaces.

In summary, optimizing embedded systems for energy, memory, and storage requires a balance of software techniques and an understanding of hardware constraints. By focusing on duty cycling, cascading design, and efficient resource management, it is possible to create effective and energy-efficient embedded applications.

Summary

The text discusses various aspects of deploying machine learning models on embedded platforms, focusing on predictive maintenance, person presence detection, and optimizing model size for microcontrollers.

Predictive Maintenance

A predictive maintenance model, such as detecting bearing failure in motors, utilizes accelerometer data to identify distinctive shaking patterns. A simple model can achieve over 95% accuracy with a small size (less than 10 KB) and minimal computational requirements. More complex models can be developed for intricate problems, but this increases parameters and operations.

Person Presence Detection

Embedded platforms are exploring computer vision applications like person presence detection to activate interfaces or other processes. The Visual Wake Word Challenge demonstrates that a 250 KB model can achieve approximately 90% accuracy with binary classification of a small monochrome image. This uses a scaled-down MobileNet v2 architecture, offering a baseline for visual tasks on microcontrollers.

Model Choice and Optimization

Starting with existing models is recommended to focus on data gathering and improvements, as machine learning software for embedded platforms is still evolving. Existing models are likely to have better support and optimization on desired devices. TensorFlow Lite for Microcontrollers can be compiled to as little as 20 KB, but requires excluding unnecessary code parts.

Measuring and Reducing Code Size

Understanding code size is crucial for optimization. On embedded platforms, the output file often includes debugging information not transferred to the device. The binary snapshot of the code, known as the bin file, represents the actual flash memory usage. Tools like arm-none-eabi-objcopy help create this binary file. Subtracting model size from the binary file length gives the real code footprint.

TensorFlow Lite Footprint

To gauge TensorFlow Lite’s space usage, remove all framework calls and note the binary size reduction. This process helps identify unused code that the linker can strip out, aiding in understanding space allocation.

OpResolver and Reducing Footprint

TensorFlow Lite supports numerous operations, but most models only require a few. The OpResolver interface allows control over which operations are linked. By customizing the OpResolver class, unnecessary operations can be excluded, reducing the code footprint. The MicroMutableOpResolver class can be used for directly adding necessary operations.

Understanding Function Sizes

Tools like nm and objdump can inspect function sizes in compiled files. This helps identify large functions that may need investigation for optimization, especially in resource-constrained environments.

Framework Constants and Truly Tiny Models

Hardcoded array sizes in library code can be adjusted to optimize RAM usage. For devices with extremely tight constraints, custom code and careful tuning are necessary. TensorFlow Lite for Microcontrollers can still be useful for training and verifying models, even if custom implementations are needed.

Conclusion

The chapter provides techniques to minimize storage requirements for embedded machine learning projects, emphasizing the importance of optimizing model and binary size to meet the constraints of microcontrollers.

In the process of deploying machine learning models, debugging is crucial to address unexpected behaviors and accuracy issues. This involves understanding the differences between training and deployment environments, especially in preprocessing steps. Preprocessing images for neural networks requires resizing and scaling, which can introduce errors if not matched precisely with the training setup. For instance, using different rescaling methods can degrade model accuracy, as seen with early Inception models that relied on bilinear scaling.

A significant challenge is ensuring that preprocessing steps, such as converting image values, align with the training environment. Discrepancies can lead to reduced accuracy, making it essential to verify these transformations during deployment. Debugging these issues involves comparing results between training and deployment, using tools and tests to ensure consistency.

On-device evaluation is another critical step, often hindered by resource constraints on embedded systems. However, ensuring that on-device accuracy matches training results is vital to avoid subtle errors. Numerical differences also arise due to varying implementations of mathematical operations across platforms. These differences must be assessed to determine their impact on the product experience.

To manage numerical differences, establishing a metric that reflects user needs is recommended. For example, using a top-one score in image classification can help track model performance. Comparing results against a baseline, such as a reference implementation, can identify discrepancies caused by optimizations.

Debugging mysterious crashes and hangs on embedded systems can be challenging. Utilizing desktop environments for initial debugging can leverage better tools and faster iterations. Additionally, log tracing is essential for understanding runtime issues, emphasizing the need for robust logging mechanisms during development.

Overall, successful deployment of machine learning models involves meticulous debugging of preprocessing, numerical differences, and runtime errors to ensure models perform as expected in real-world applications.

Debugging Embedded Systems

Fault Handling and Logging: Implementing fault handlers and injecting log statements can help trace code execution. A macro like #define TRACE DebugLog(__FILE__ ":" __LINE__) can be used to track code progress.

Shotgun Debugging: When tracing isn’t enough, use “shotgun debugging” by commenting out sections of code to isolate errors, similar to binary search. This helps identify problematic code areas.

Memory Corruption: Embedded systems lack hardware protection against memory overwrites, making debugging challenging. Stack overruns are common, especially with TensorFlow Lite for Microcontrollers, which requires significant stack space. Increasing stack size and using logging can help identify corrupted variables.

Porting Models to TensorFlow Lite

Understanding Operations: TensorFlow Lite supports a subset of TensorFlow operations. Use tf.keras.backend.get_session() to inspect model operations and ensure compatibility. Regularly check the ops compatibility guide to avoid unsupported features.

Preprocessing and Postprocessing: Implement these stages as application code rather than embedding them in the TensorFlow Lite model. This approach is more efficient and allows leveraging device-specific optimizations.

Handling Unsupported Ops: If necessary, unsupported operations can be saved as custom ops and implemented within the TensorFlow Lite framework using AddCustom().

Optimizing Operations: Ensure operations are optimized for performance. Check export compatibility early to allocate time for potential optimization.

Privacy and Security

Privacy Concerns: On-device machine learning uses sensor inputs, raising privacy issues. Engineers must consider privacy at all stages and consult legal experts as necessary.

Privacy Design Document (PDD): Developing a PDD can help address privacy implications throughout the project lifecycle. Regular reviews ensure ongoing compliance and protection.

By methodically applying these debugging and optimization techniques, and considering privacy implications, developers can effectively manage embedded machine learning challenges.

Privacy Design Document (PDD)

Large companies often use a Privacy Design Document to manage privacy in their applications. This document centralizes key privacy aspects, including:

• Data Collection: Detail what data is gathered, how, and why, using clear language. Ensure data collection is minimal and necessary. Consider using simple system diagrams to illustrate data flow. Assess potential misuse scenarios to enhance user protection.

• Data Usage: Clearly define data usage post-collection. Avoid using user data for machine learning without explicit consent. On-device processing can enhance privacy by sharing only aggregated results. Design hardware to enforce privacy guarantees.

• Data Sharing and Storage: Limit data access, storage duration, and ensure secure deletion policies. Treat all sensor data as personally identifiable information (PII) and minimize data retention to reduce risks.

• Consent: Ensure users understand and agree to data collection and usage. Communicate product benefits transparently to facilitate informed user consent.

Security

Securing embedded devices is challenging due to potential physical access by attackers. Key strategies include:

• Minimize sensitive data retention on devices.
• Use secure cryptoprocessors for key management and secure booting.
• Design hardware to limit attack opportunities, e.g., avoid unnecessary wireless capabilities.
• Rely on established libraries for security to avoid errors in custom cryptography.
• Develop a security design document to anticipate and mitigate potential attacks.

Protecting Machine Learning Models

Protecting machine learning models from competitors is difficult. Models, like software, can be copied. However, attackers cannot easily modify models without access to training data. Techniques to deter casual theft include encrypting models and embedding subtle flaws to detect unauthorized use.

Deployment

Over-the-air updates increase security risks. It’s recommended to avoid post-manufacture updates to minimize attack surfaces. Ensure code accuracy before release and use real-world testing to identify issues. Develop institutional memory through test cases derived from real-world observations.

Moving to Production

Transitioning from development boards to production requires cost considerations and ensuring compatibility. Debugging becomes harder post-deployment, so delay final deployment until necessary.

Conclusion

Safeguarding privacy and security is a critical responsibility for engineers. The document outlines foundational strategies for building and deploying secure, privacy-conscious embedded machine learning applications.

Further Learning

For ongoing updates and learning:

• TinyML Foundation: Engage in annual conferences and monthly meetups.
• SIG Micro: Collaborate on TensorFlow Lite for Microcontrollers improvements.
• TensorFlow Website: Access the latest examples and documentation.
• Other Frameworks: Explore alternatives like uTensor and Microsoft’s Embedded Learning Library.
• Twitter: Share projects usingtinyml and follow updates from @tinymlbook.

Explore offerings from various companies in the field for additional resources and support.

Summary

This document provides guidance on working with machine learning on embedded devices, specifically focusing on the integration of TensorFlow Lite for Microcontrollers with platforms like Arduino. It highlights collaborations with organizations such as Adafruit, Arm, and Qualcomm, and aims to inspire readers to explore projects in this field.

Generating an Arduino Library Zip

To use TensorFlow Lite for Microcontrollers with the Arduino IDE, source files must be packaged as a .zip library. The TensorFlow team provides prebuilt .zip files generated nightly, which can be imported into the Arduino IDE. Users can also generate their own .zip files by cloning the TensorFlow repository and running a specific script. Importing the library into the Arduino IDE involves using the “Add .ZIP Library” option. If modifications are made to the code, the library must be regenerated and re-imported.

Capturing Audio on Arduino

The document details the process of capturing audio using the Arduino Nano 33 BLE Sense’s onboard microphone. Audio data is received in chunks and stored in a buffer, which is overwritten when full. The process involves registering a callback function to handle new audio data, using the PDM library to capture audio, and managing a buffer to store and process the data.

Key functions include GetAudioSamples(), which provides access to raw audio data, and LatestAudioTimestamp(), which returns the timestamp of the most recent audio sample. The audio capture buffer is designed to handle data efficiently, ensuring enough new data is available for preprocessing and inference.

Application Building Examples

Magic Wand Example

The magic wand example involves deploying a gesture detection model on microcontrollers. It covers the application architecture, capturing accelerometer data, and handling gestures. The document provides steps for deploying the model on platforms like Arduino and SparkFun Edge, along with tests and responding to gestures.

Person Detection Example

This example focuses on building a person detection application. It details the application architecture, capturing images, and deploying the model on Arduino and SparkFun Edge. The process includes setting up the camera module, responding to detections, and optimizing model and binary size.

Designing Your Own Applications

The document encourages designing custom applications by understanding data requirements, feature generation, and reviewing similar models. It suggests starting on a desktop to refine the application before deploying it on microcontrollers.

Overall, the document serves as a comprehensive guide for integrating machine learning with embedded devices, offering practical steps and examples to help users build and deploy their own applications.

Summary of TinyML: Wake-Word Detection and Application Building

Application Building and Architecture: The process of building applications, particularly wake-word detection, involves several components and steps. Key components include the application architecture, audio provider, command responder, and feature provider. The basic flow involves listening for wake words, optimizing models, and deploying to microcontrollers like Arduino and SparkFun Edge.

Microcontroller Deployment: Deployment to microcontrollers is crucial for running applications efficiently. This involves handling outputs, responding to commands and detections, and optimizing for specific hardware like the ST Microelectronics STM32F746G Discovery Kit. Differences in Arduino example code and handling outputs are highlighted.

Model Training and Optimization: Training involves collecting datasets, designing model architecture, and running inference. Optimizing model size and binary size is essential, especially for microcontrollers with limited resources. Techniques include quantization and removing unused operations. Debugging and testing are integral to ensure accuracy and performance.

TensorFlow and TensorFlow Lite: TensorFlow Lite is used for converting models to run on microcontrollers. Porting models involves understanding required operations, optimizing them, and integrating with TensorFlow Lite for Microcontrollers. This process includes creating interpreters, defining tensor arenas, and evaluating models.

Debugging and Testing: Debugging focuses on resolving accuracy loss, preprocessing differences, and memory corruption issues. Testing involves setting up environments, running inference, and verifying outputs. Writing tests and using tools like TensorBoard and logic analyzers are part of the workflow.

Performance and Power Optimization: Optimizing latency and power usage is critical for embedded devices. This includes using accelerators, profiling performance, and understanding hardware features. Power optimization involves duty cycling and selecting efficient hardware components.

Privacy and Security: Privacy concerns are addressed through privacy design documents, which cover data collection, sharing, and consent. Security measures ensure the protection of personally identifiable information.

Tools and Platforms: The book covers various tools and platforms, including Google Colaboratory for training, and IDEs for development. It emphasizes the integration of machine learning toolchains like TensorFlow and Keras.

Authors and Additional Information: The authors, Pete Warden and Daniel Situnayake, are experts in mobile and embedded TensorFlow. The book includes insights from their experiences and contributions to the field of TinyML.

This summary captures the essential aspects of building, deploying, and optimizing wake-word detection applications using TinyML, with a focus on practical implementation and performance considerations.