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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key characteristics of TinyML model deployment
- Limitations within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Detecting memory-intensive operations
- Establishing baseline performance profiles
Quantization Techniques
- Post-training quantization strategies
- Quantization-aware training methods
- Assessing the balance between accuracy and resource usage
Pruning and Compression
- Structured and unstructured pruning approaches
- Weight sharing and model sparsity concepts
- Compression algorithms for lightweight inference
Hardware-Aware Optimization
- Model deployment on ARM Cortex-M systems
- Leveraging DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Validation
- Analyzing 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 physical hardware
Advanced Optimization Strategies
- Neural architecture search for TinyML
- Hybrid quantization-pruning techniques
- Model distillation for embedded inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Practical experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Audience
- AI researchers
- Embedded ML engineers
- Professionals developing inference systems under resource constraints
