Optimization of AI Models for Embedded Systems
( 30 Modules )

Module #1
Introduction to AI in Embedded Systems
Overview of AI applications in embedded systems, challenges, and opportunities

Module #2
Basics of AI and Machine Learning
Review of AI and ML concepts, including neural networks, deep learning, and model evaluation

Module #3
Embedded Systems Fundamentals
Overview of embedded systems, including hardware and software components, and constraints

Module #4
AI Model Optimization Fundamentals
Introduction to model optimization techniques, including model pruning, quantization, and knowledge distillation

Module #5
Design Considerations for AI-Enabled Embedded Systems
Discussion of design considerations, including power consumption, memory constraints, and real-time requirements

Module #6
Model Pruning and Compression
In-depth discussion of model pruning and compression techniques, including magnitude-based pruning and Huffman coding

Module #7
Quantization and Bit-Width Reduction
Quantization techniques, including uniform and non-uniform quantization, and bit-width reduction

Module #8
Knowledge Distillation and Model Hints
Knowledge distillation and model hints for model optimization

Module #9
Low-Precision Data Types and Numerical Formats
Use of low-precision data types and numerical formats, including float16 and int8

Module #10
Sparsity and Structured Pruning
Techniques for inducing sparsity and structured pruning in AI models

Module #11
Understanding Embedded System Hardware
Overview of embedded system hardware, including CPUs, GPUs, and specialized accelerators

Module #12
Hardware-Aware Model Design
Designing AI models with awareness of embedded system hardware constraints

Module #13
Optimizing AI Models for Specific Hardware
Optimizing AI models for specific embedded system hardware, including ARM, x86, and RISC-V

Module #14
Exploiting Parallelism and Pipelining
Techniques for exploiting parallelism and pipelining in AI model execution

Module #15
Energy Efficiency and Power Optimization
Optimizing AI models for energy efficiency and power consumption in embedded systems

Module #16
Understanding Embedded System Software
Overview of embedded system software, including operating systems and device drivers

Module #17
Software-Aware Model Optimization
Optimizing AI models with awareness of embedded system software constraints

Module #18
Optimizing AI Models for Real-Time Systems
Optimizing AI models for real-time systems and meeting latency and jitter requirements

Module #19
Memory Optimization and Cache Efficiency
Optimizing AI models for memory efficiency and cache utilization

Module #20
Debugging and Profiling AI Models on Embedded Systems
Tools and techniques for debugging and profiling AI models on embedded systems

Module #21
Distributed AI and Federated Learning
Distributed AI and federated learning for embedded systems

Module #22
Explainability and Interpretability in AI Models
Techniques for explainability and interpretability in AI models for embedded systems

Module #23
Model Robustness and Adversarial Attacks
Optimizing AI models for robustness and defending against adversarial attacks

Module #24
Ethics and Fairness in AI Model Optimization
Ethical considerations and fairness in AI model optimization for embedded systems

Module #25
Future Directions in AI Model Optimization for Embedded Systems
Overview of emerging trends and future directions in AI model optimization for embedded systems

Module #26
Project 1:Optimizing a Simple AI Model for Embedded Systems
Hands-on project optimizing a simple AI model for embedded systems

Module #27
Case Study 1:AI-Enabled Smart Camera
Case study of an AI-enabled smart camera system, including optimization techniques and challenges

Module #28
Project 2:Optimizing a Complex AI Model for Embedded Systems
Hands-on project optimizing a complex AI model for embedded systems

Module #29
Case Study 2:AI-Enabled Autonomous Vehicle
Case study of an AI-enabled autonomous vehicle system, including optimization techniques and challenges

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Optimization of AI Models for Embedded Systems career

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Optimization of AI Models for Embedded Systems ( 30 Modules )

Optimization of AI Models for Embedded Systems
( 30 Modules )