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Course Outline
Introduction to TinyML and Embedded AI
- Key characteristics of TinyML model deployment
- Constraints within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Understanding computational bottlenecks
- Identifying memory-intensive operations
- Baseline performance profiling
Quantization Techniques
- Strategies for post-training quantization
- Quantization-aware training
- Evaluating the trade-off between accuracy and resource usage
Pruning and Compression
- Methods for structured and unstructured pruning
- Weight sharing and model sparsity
- Compression algorithms for lightweight inference
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
- Analysis of latency and throughput
- Measurements of 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
- Hybrid approaches combining quantization and pruning
- Model distillation for embedded inference
Summary and Next Steps
Requirements
- Familiarity with machine learning workflows
- Experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals working on resource-constrained inference systems
21 Hours
