๐Ÿ“ฑ Edge AI: Complete Guide

On-Device Intelligence for Privacy, Speed, and Offline Capability

ๅผ˜็›Šไบบ้–“ (Hongik Ingan)

"Widely Benefit All Humanity" - Bringing AI to the edge empowers individuals with privacy-preserving, always-available intelligence

CHAPTER 1
Introduction to Edge AI
Understanding edge computing, benefits of on-device AI, and the edge AI landscape
CHAPTER 2
Edge AI Architecture
System design, hardware components, software stack, and deployment patterns
CHAPTER 3
Model Optimization
Quantization, pruning, knowledge distillation, and compression techniques
CHAPTER 4
TinyML and Embedded AI
Ultra-low-power AI for microcontrollers, TensorFlow Lite Micro, and IoT applications
CHAPTER 5
Hardware Accelerators
NPUs, GPUs, DSPs, Edge TPUs, and specialized AI chips
CHAPTER 6
Federated Edge Learning
Collaborative learning across edge devices, privacy-preserving model updates
CHAPTER 7
Privacy and Security
On-device privacy, secure enclaves, model protection, and threat mitigation
CHAPTER 8
Production Deployment
Deployment strategies, monitoring, updates, and real-world case studies