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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key features of TinyML model deployment
- Limits specific to microcontroller environments
- Introduction to embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Detecting memory-intensive operations
- Conducting baseline performance profiling
Quantization Methods
- Post-training quantization strategies
- Quantization-aware training
- Assessing the balance between accuracy and resource usage
Pruning and Compression
- Structured and unstructured pruning techniques
- Weight sharing and model sparsity
- Compression algorithms for lightweight inference
Hardware-Centric Optimization
- Model deployment on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Verification
- Analysis of latency and throughput
- Measurement of power and energy consumption
- Testing for accuracy and robustness
Deployment Processes and Tools
- Leveraging TensorFlow Lite Micro for embedded use cases
- Integrating TinyML models with Edge Impulse workflows
- Testing and debugging on physical hardware
Advanced Optimization Tactics
- Neural architecture search tailored for TinyML
- Combined quantization-pruning methods
- Model distillation for embedded inference
Recap and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals focused on resource-constrained inference systems
