Deep Learning on Microcontrollers: Learn how to develop embedded AI applications using TinyML

Description

A step-by-step guide that will teach you how to deploy TinyML on microcontrollers. Key Features: - Deploy machine learning models on edge devices with ease. - Leverage pre-built AI models and deploy them without writing any code. - Create smart and efficient IoT solutions with TinyML. Description: TinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you. This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers. The book will then help you build an Air gesture digit recognition system using the Arduino Nano RP2040 board and an AI project for recognizing keywords using the Syntiant TinyML board. Lastly, the book summarizes the concepts covered and provides a brief introduction to topics such as zero-shot learning, one-shot learning, federated learning, and MLOps. By the end of the book, you will be able to develop and deploy end-to-end Tiny ML solutions with ease. What you will learn: - Learn how to build a Keyword recognition system using the Syntiant TinyML board. - Learn how to build an air gesture digit recognition system using the Arduino Nano RP2040. - Learn how to test and deploy models on Edge Impulse and Arduino IDE. - Get tips to enhance system-level performance. - Explore different real-world use cases of TinyML across various industries. Who this book is for: The book is for IoT developers, System engineers, Software engineers, Hardware engineers, and professionals who are interested in integrating AI into their work. This book is a valuable resource for Engineering undergraduates who are interested in learning about microcontrollers and IoT devices but may not know where to begin. Introduction to AI Introduction Structure Objectives Artificial Intelligence Continuum of code writing and artificial intelligence Exercise Changing the paradigm Neural Network Machine Learning Intelligent IoT System vs. Cloud based IoT system Arduino Nano 33 BLE Sense board Limited compute resources Battery power limits TinyML and Nicla Voice board >10x parameters >200x Power advantage >20x Throughput TinyML Ecosystem Key applications for Intelligent IoT systems Smart agriculture Smart appliances Smart cities Smart health Smart homes Smart industry Conclusion Key facts Questions References 2. Traditional ML Lifecycle Introduction Structure Objectives Traditional methods Machine learning landscape Supervised learning Unsupervised learning Reinforcement Learning (RL) ML Performance Metrics Confusion matrix Basics of DL and different DL algorithms Transfer Learning Tools and Different ML, DL frameworks Python Jupyter Notebooks Google Colaboratory TensorFlow (TF), TFLite and TensorFlow Lite Micro TensorFlow Lite TensorFlow Lite Micro AI Model Efficiency Toolkit (AIMET) Convolutional Architecture for Fast Feature Embedding (Caffe) CoreML Open Neural Network Exchange (ONNX) Open Visual Inference and Neural network Optimization (OpenVINO) Pytorch and PyTorch Mobile Embedded Machine Learning (EML) Difference between Learning and Inference ML model deployment and inferencing on different platforms Conclusion Key facts Questions References 3. TinyML Hardware and Software Platforms Introduction Structure Objectives Servers at Data Centers: CPUs, GPUs and TPUs Mobile CPU, Raspberry Pi board and its types Microcontrollers and Microcontroller with AI accelerator TinyML Hardware Boards Arduino and Arduino Nano 33 BLE Arduino Nicla Sense ME Adafruit Feather SparkFun Edge NVIDIA Jetson Nano Google Coral Edge TPU Qualcomm QCS605 NXP i.MX 8M STMicroelectronics STM32L4 Intel Curie Syntiant TinyML TinyML Software Suites TensorFlow Lite Micro (Google) uTensor (ARM) Arduino Create EloquentML EdgeML (Microsoft) EON Compiler (Edge Impulse) STM32Cube.AI and NanoEdge AI Studio (STMicroelectronics) PYNQ OpenMV SensiML Neuton TinyML Metavision Intelligence Suite 3.0 (Vision applications) Data Engineering Frameworks Edge Impulse SensiML Qeexo AutoML TinyML Model Compression Frameworks Quantization Pruning Low ranked approximation Knowledge distillation TensorFlow Lite STM32 X-CUBE-AI QKeras Qualcomm AIMET Microsoft NNI CMix-NN OmniML Conclusion Key facts Questions References 4. End-to-End TinyML Deployment Phases Introduction Structure Objectives Understanding Embedded ML Introduction to Edge-impulse and Arduino IDE Edge-impulse Arduino Integrated Development Environment (IDE) Arduino Driver Installation Data collection from multiple sensors Data collection from an Arduino board Data collection from Syntiant board Data engineering steps for TinyML Cleaning Organizing Transformation Model Training in TinyML software platforms EON Compiler (Edge Impulse) Model Compression Pruning Knowledge distillation Model conversion Quantization Inferencing/Prediction of results with test data Model Deployment in TinyML Hardware board Conclusion Key facts Questions References 5. Real World Use Cases Introduction Structure Objectives Smart agriculture Agriculture video analytics Crop intruder detection Crop yield prediction and improvement Agribots Insect detection and pesticide reduction Weedicides elimination Acoustic insect detection Animal husbandry Smart appliances Vision AI for appliances Audio AI for appliances Sensors based AI for appliances Smart cities Safe and secure city City maintenance Parking enforcement systems Traffic management Maintaining bridges Non-Smoking enforcement Smart health Cataract detection Fall detection Cough detection Boxing Moves Detector Mosquito detection Snoring and sleep apnea detection Smart home Person detection at the door Glassbreak detection Smart baby monitoring Voice recognition for home automation Smart industry Railway track defect detection Telecom towers defect detection Defect detection in components Smart automotive Drowsy driver alert Advance collision detection Conclusion Key facts Questions References 6. Practical Experiments with TinyML Introduction Structure Objectives Introduction to Nano RP2040 TinyML board Setting up Arduino IDE and testing the Nano RP2040 Board High level steps involved in the air gesture digit recognition in Edge Impulse platform Data collection for the air gesture digit recognition Loading the dataset in Edge Impulse Platform Setting up the development framework and design of neural network classifier Model training in Edge Impulse platform Model testing with the collected data Model deployment in Nano RP2040 board Inferencing/Prediction of results with RP2040 Conclusion Key facts Questions References 7. Advance Implementation with TinyML Board Introduction Structure Objectives NDP101 Architecture NDP120 Architecture Practical implementation and deployment Creating a project Uploading Data Impulse Design Epochs Setting Learning rate setting Validation data set setting Auto balance setting Data augmentation Neural network architecture Neural network training Model testing Deployment Conclusion Key facts Questions References 8. Continuous Improvement Introduction Structure Objectives Expectation gap Unique issues about audio application Raw neural network output and softmax transformation Handling anomalous behavior during target classifier testing Method 1: Running window averaging Method 2: Enriching target classifier Method 3: Enriching open set classifier False Acceptance Rate testing Optimization of window size in running window averaging Phrase recognition constraints to improve system level performance FRR testing under noisy conditions Improving FRR performance under noisy conditions Data collection for continuous improvement Conclusion Key facts Questions References 9. Conclusion Introduction Structure Objectives Review of material covered in this book Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Advanced topics Different types of neural networks Neural network optimization Zero-shot, One-shot or Few-shot learning Federated learning Transfer learning Tuning pretrained networks MLOps Key facts Questions References