目錄
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
4.Algorithms for Edge AI
Feature Engineering
Working with Data Streams
Digital Signal Processing Algorithms
Combining Features and Sensors
Artificial Intelligence Algorithms
Algorithm Types by Functionality
Algorithm Types by Implementation
Optimization for Edge Devices
On-Device Training
Summary
5.Tools and Expertise
Building a Team for AI at the Edge
Domain Expertise
6.Understanding and Framing Problems.
7.How to Build a Dataset
8.Designing Edge AI Applications
9.Developing Edge AI Applications.
10.Evaluatinq, Deploying, and Supporting Edge AI Applications
11.Use Case: Wildlife Monitoring
12.Use Case: Food Quality Assurance
13.Use Case: Consumer Products
Index
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