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TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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About TinyML Cookbook: Combine Artificial Intelligence
Review "This book is a nice snapshot of the fast-growing branch of AI dealing with extreme energy-efficient machine learning, tinyML. Not only does it highlight the current landscape of tinyML technologies and opportunities, but it also serves as a step-by-step practical guide to getting up to speed with developing several applications on your own. Definitely worth reading to explore the amazing world of tinyML!" --Evgeni Gousev, Chairman, tinyML Foundation, and Sr. Director, Qualcomm AI Research Product Description Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learningKey FeaturesTrain and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi PicoWork with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge ImpulseExplore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPUBook DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers.The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game.By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.What you will learnUnderstand the relevant microcontroller programming fundamentalsWork with real-world sensors such as the microphone, camera, and accelerometerRun on-device machine learning with TensorFlow Lite for MicrocontrollersImplement an app that responds to human voice with Edge ImpulseLeverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE SenseCreate a gesture-recognition app with Raspberry Pi PicoDesign a CIFAR-10 model for memory-constrained microcontrollersRun an image classifier on a virtual Arm Ethos-U55 microNPU with microTVMWho this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.Table of ContentsGetting Started with TinyMLPrototyping with MicrocontrollersBuilding a Weather Station with TensorFlow Lite for MicrocontrollersVoice Controlling LEDs with Edge ImpulseIndoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino NanoBuilding a Gesture-Based Interface for YouTube PlaybackRunning a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OSToward the Next TinyML Generation with microNPU About the Author Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Ma
