1.1
Definition and Principles of Edge AI and Computing
1.2
Evolution of Edge Computing and Its Relationship with Cloud Computing
1.3
Key Drivers and Benefits of Edge AI
1.4
Edge AI Ecosystem and Architecture Overview
2.1
Basic Concepts of AI and ML
2.2
Supervised, Unsupervised, and Reinforcement Learning
2.3
Common ML Algorithms and Their Applications
2.4
Feature Engineering and Data Preprocessing
3.1
Fundamentals of Neural Networks
3.2
Convolutional Neural Networks (CNNs)
3.3
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
3.4
Transfer Learning and Pre-trained Models
4.1
Resource Constraints in Edge Devices
4.2
Scalability and Heterogeneity Issues
4.3
Security and Privacy Concerns
4.4
Opportunities for Real-time Processing and Low Latency
5.1
Overview of Model Compression Approaches
5.2
Knowledge Distillation
5.3
Low-Rank Approximation and Tensor Decomposition
6.1
Fundamentals of Quantization
6.2
Post-training Quantization vs. Quantization-Aware Training
6.3
Network Pruning Techniques
6.4
Sparse Neural Networks
7.1
Overview of Edge AI Hardware Platforms
7.2
GPU-based Accelerators for Edge Devices
7.3
FPGA and ASIC Solutions for Edge AI
7.4
Neuromorphic Computing Hardware
8.1
Power Consumption Challenges in Edge Devices
8.2
Low-Power Design Techniques for Edge AI
8.3
Energy-Aware Algorithm Design
8.4
Dynamic Voltage and Frequency Scaling (DVFS)
9.1
Principles of Distributed Inference
9.2
Federated Learning for Edge Devices
9.3
Swarm Intelligence and Multi-Agent Systems
10.1
Real-Time System Requirements for Edge AI
10.2
Latency Optimization Techniques
10.3
Pipelining and Parallelism in Edge Computing
10.4
Memory Management for Low-Latency Inference
11.1
Privacy Challenges in Edge AI
11.2
Differential Privacy for Edge Computing
11.3
Homomorphic Encryption and Secure Multi-Party Computation
11.4
Blockchain for Secure Edge AI
12.1
Edge-Cloud Continuum and Fog Computing
12.2
Workload Partitioning between Edge and Cloud
12.3
Data Synchronization and Consistency
12.4
Edge-Cloud Communication Protocols
13.1
IoT Architectures and Edge AI Integration
13.2
Smart Home and Building Automation
13.3
Industrial IoT and Predictive Maintenance
13.4
Edge AI in Healthcare and Wearable Devices
14.1
Edge AI for Autonomous Vehicles
14.2
Robotics and Edge Computing
14.3
Drones and Unmanned Aerial Vehicles (UAVs)
14.4
Edge AI in Smart Cities and Traffic Management
15.1
Mobile AI Frameworks and Libraries
15.2
On-Device Learning and Adaptation
15.3
Edge AI for Augmented and Virtual Reality
15.4
Deployment Strategies and Best Practices for Mobile Edge AI