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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key features of TinyML deployment
- Limitations within microcontroller environments
- Overview of embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Detecting memory-intensive operations
- Establishing baseline performance profiles
Quantization Methods
- Post-training quantization strategies
- Quantization-aware training
- Weighing accuracy against resource usage
Pruning and Compression
- Structured and unstructured pruning techniques
- Weight sharing and model sparsity
- Compression algorithms for lightweight inference
Hardware-Specific Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considering memory mapping and dataflow
Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing 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 physical hardware
Advanced Optimization Strategies
- Applying neural architecture search to TinyML
- Combining quantization and pruning
- Using model distillation for embedded inference
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Experience with embedded systems or microcontroller development
- Proficiency in Python programming
Audience
- AI researchers
- Embedded ML engineers
- Professionals handling inference systems with limited resources
