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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key features of TinyML model deployment
- Limitations within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Recognizing memory-intensive operations
- Establishing baseline performance profiles
Quantization Strategies
- Post-training quantization approaches
- Quantization-aware training methods
- Balancing accuracy against resource usage
Pruning and Compression Techniques
- Structured and unstructured pruning methods
- Weight sharing and model sparsity concepts
- Compression algorithms for lightweight inference
Hardware-Aware Optimization
- Deploying models on ARM Cortex-M architectures
- Leveraging DSP and accelerator extensions
- Considering memory mapping and dataflow structures
Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing accuracy and robustness
Deployment Workflows and Tools
- Using TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse workflows
- Testing and debugging on physical hardware
Advanced Optimization Strategies
- Neural architecture search for TinyML
- Hybrid approaches combining quantization and pruning
- Model distillation for embedded inference
Conclusion and Future Steps
Requirements
- Foundational knowledge of machine learning workflows
- Practical experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals developing resource-constrained inference systems
