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Duration 21 hours
Course Outline
Foundations of TinyML and Embedded AI
- Key attributes of TinyML model deployment
- Limitations within microcontroller environments
- Introduction to embedded AI toolchains
Core Principles of Model Optimization
- Identifying computational bottlenecks
- Recognizing memory-intensive operations
- Establishing baseline performance profiles
Quantization Methods
- Post-training quantization approaches
- Quantization-aware training techniques
- Assessing the balance between accuracy and resource usage
Pruning and Compression Strategies
- Structured and unstructured pruning techniques
- Weight sharing and model sparsity
- Compression algorithms for efficient lightweight inference
Hardware-Centric Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Performance Benchmarking and Validation
- Analysis of latency and throughput
- Measurement of power and energy consumption
- Testing for accuracy and robustness
Deployment Processes and Tooling
- Utilizing TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse workflows
- Testing and debugging on physical hardware
Advanced Optimization Tactics
- Neural architecture search tailored for TinyML
- Combined quantization and pruning approaches
- Model distillation for embedded inference
Conclusion and Future Directions
Requirements
- Knowledge of machine learning workflows
- Hands-on experience with embedded systems or microcontroller development
- Proficiency in Python programming
Target Audience
- AI Researchers
- Embedded Machine Learning Engineers
- Professionals specializing in resource-constrained inference systems
