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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key attributes of TinyML model deployment
- Specific constraints in microcontroller environments
- Introduction to embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Recognizing memory-intensive operations
- Establishing baseline performance profiles
Quantization Techniques
- Strategies for post-training quantization
- Quantization-aware training methodologies
- Balancing accuracy against resource usage
Pruning and Compression
- Methods for structured and unstructured pruning
- Implementing weight sharing and model sparsity
- Compression algorithms for lightweight inference
Hardware-Aware Optimization
- Deploying models on ARM Cortex-M systems
- Leveraging DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing accuracy and robustness
Deployment Workflows and Tools
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse pipelines
- Conducting tests 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 grasp of machine learning workflows
- Experience in embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals specializing in resource-constrained inference systems
