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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key features of TinyML model deployment
- Limitations within microcontroller environments
- Introduction to embedded AI toolchains
Foundations of Model Optimization
- Recognizing computational bottlenecks
- Identifying operations that consume significant memory
- Establishing baseline performance profiles
Quantization Methods
- Post-training quantization strategies
- Quantization-aware training processes
- Balancing accuracy with resource utilization
Pruning and Compression
- Structured versus unstructured pruning techniques
- Weight sharing and model sparsity concepts
- Compression algorithms for lightweight inference
Hardware-Centric Optimization
- Model deployment on ARM Cortex-M systems
- Optimization for DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Validation
- Analysis of latency and throughput
- Measurement of power and energy consumption
- Testing for accuracy and system robustness
Deployment Workflows and Tooling
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse workflows
- Testing and debugging on physical hardware
Advanced Optimization Strategies
- Neural architecture search for TinyML
- Combined quantization and pruning methods
- Model distillation for embedded inference
Conclusion and Future Directions
Requirements
- Knowledge of machine learning workflows
- Experience with embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals focused on resource-constrained inference systems
