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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key features of TinyML model deployment
- Limits within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Recognizing computational bottlenecks
- Identifying operations that consume significant memory
- Establishing baseline performance profiles
Quantization Approaches
- Post-training quantization strategies
- Quantization-aware training methods
- Assessing the trade-off between accuracy and resource usage
Pruning and Compression Methods
- Structured and unstructured pruning techniques
- Weight sharing and achieving model sparsity
- Compression algorithms designed for lightweight inference
Hardware-Specific Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considerations for memory mapping and data flow
Benchmarking and Validation
- Analysis of latency and throughput
- Measuring power and energy consumption
- Testing for accuracy and robustness
Deployment Processes and Tools
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Testing and debugging on physical hardware
Advanced Optimization Techniques
- Neural architecture search for TinyML
- Hybrid approaches combining quantization and pruning
- Model distillation for embedded inference
Conclusion and Future Steps
Requirements
- A solid grasp of machine learning workflows
- Hands-on 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
