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Foreword Preface 1.A Brief Introduction to Edge AI Defining Key Terms Embedded The Edge (and the Internet of Things) Artificial Intelligence Machine Learning Edge AI Embedded Machine Learning and Tiny Machine Learning Digital Signal Processing Why Do We Need Edge AI? To Understand the Benefits of Edge AI, Just BLERP Edge AI for Good Key Differences Between Edge AI and Regular AI Summary 2.Edge AI in the Real World Common Use Cases for Edge AI Greenfield and Brownfield Projects Real-World Products Types of Applications Keeping Track of Objects Understanding and Controlling Systems Understanding People and Living Things Transforming Signals Building Applications Responsibly Responsible Design and AI Ethics Black Boxes and Bias Technology That Harms, Not Helps Summary 3.The Hardware of Edge AI Sensors, Signals, and Sources of Data Types of Sensors and Signals Acoustic and Vibration Visual and Scene Motion and Position Force and Tactile Optical, Electromagnetic, and Radiation Environmental, Biological, and Chemical Other Signals Processors for Edge AI Edge AI Hardware Architecture Microcontrollers and Digital Signal Processors System-on-Chip Deep Learning Accelerators FPGAs and ASICs Edge Servers Multi-Device Architectures Devices and Workloads Summary