🤖Edge AI and Computing

Definition and Principles of Edge AI and Computing

Evolution of Edge Computing and Its Relationship with Cloud Computing

Key Drivers and Benefits of Edge AI

Edge AI Ecosystem and Architecture Overview

Basic Concepts of AI and ML

Supervised, Unsupervised, and Reinforcement Learning

Common ML Algorithms and Their Applications

Feature Engineering and Data Preprocessing

Fundamentals of Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)

Transfer Learning and Pre-trained Models

Resource Constraints in Edge Devices

Scalability and Heterogeneity Issues

Security and Privacy Concerns

Opportunities for Real-time Processing and Low Latency

Overview of Model Compression Approaches

Knowledge Distillation

Low-Rank Approximation and Tensor Decomposition

Fundamentals of Quantization

Post-training Quantization vs. Quantization-Aware Training

Network Pruning Techniques

Sparse Neural Networks

Overview of Edge AI Hardware Platforms

GPU-based Accelerators for Edge Devices

FPGA and ASIC Solutions for Edge AI

Neuromorphic Computing Hardware

Power Consumption Challenges in Edge Devices

Low-Power Design Techniques for Edge AI

Energy-Aware Algorithm Design

Dynamic Voltage and Frequency Scaling (DVFS)

Principles of Distributed Inference

Federated Learning for Edge Devices

Swarm Intelligence and Multi-Agent Systems

Real-Time System Requirements for Edge AI

Latency Optimization Techniques

Pipelining and Parallelism in Edge Computing

Memory Management for Low-Latency Inference

Privacy Challenges in Edge AI

Differential Privacy for Edge Computing

Homomorphic Encryption and Secure Multi-Party Computation

Blockchain for Secure Edge AI

Edge-Cloud Continuum and Fog Computing

Workload Partitioning between Edge and Cloud

Data Synchronization and Consistency

Edge-Cloud Communication Protocols

IoT Architectures and Edge AI Integration

Smart Home and Building Automation

Industrial IoT and Predictive Maintenance

Edge AI in Healthcare and Wearable Devices

Edge AI for Autonomous Vehicles

Robotics and Edge Computing

Drones and Unmanned Aerial Vehicles (UAVs)

Edge AI in Smart Cities and Traffic Management

Mobile AI Frameworks and Libraries

On-Device Learning and Adaptation

Edge AI for Augmented and Virtual Reality

Deployment Strategies and Best Practices for Mobile Edge AI