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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Core features of deploying TinyML models
- Limitations within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Understanding computational bottlenecks
- Identifying operations that consume significant memory
- Baseline performance profiling
Quantization Techniques
- Strategies for post-training quantization
- Quantization-aware training
- Assessing the balance between accuracy and resource usage
Pruning and Compression
- Methods for structured and unstructured pruning
- Utilizing weight sharing and model sparsity
- Algorithms for compressing lightweight inference models
Hardware-Aware Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for 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
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Testing and debugging on physical hardware
Advanced Optimization Strategies
- Applying neural architecture search to TinyML
- Combining quantization and pruning approaches
- Using model distillation for embedded inference
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows
- Experience with embedded systems or microcontroller-based development
- Proficiency in Python programming
Audience
- AI researchers
- Embedded ML engineers
- Professionals focused on resource-constrained inference systems
