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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
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Understanding computational bottlenecks
- Identifying operations with high memory demand
- Baseline performance analysis
Quantization Methods
- Post-training quantization approaches
- Quantization-aware training
- Assessing the trade-off between accuracy and resource usage
Pruning and Compression
- Structured and unstructured pruning techniques
- Weight sharing and model sparsity
- Compression algorithms for efficient inference
Hardware-Specific Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Validation
- Analysis of latency and throughput
- Measuring power and energy consumption
- Testing for accuracy and robustness
Deployment Workflows and Tools
- Utilizing TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Testing and debugging on actual hardware
Advanced Optimization Strategies
- Neural architecture search for TinyML
- Combining quantization and pruning approaches
- Model distillation for embedded inference
Summary and Next Steps
Requirements
- Knowledge of machine learning processes
- Experience with embedded systems or microcontroller development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals focused on resource-constrained inference systems
