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Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key aspects of TinyML model deployment
- Limitations in 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
- Assessing accuracy versus resource trade-offs
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
- Considerations for memory mapping and dataflow
Benchmarking and Verification
- Analysis of latency and throughput
- Measurement of power and energy consumption
- Testing for accuracy and robustness
Deployment Processes and Tools
- Leveraging TensorFlow Lite Micro for embedded deployment
- Integrating TinyML models with Edge Impulse workflows
- Testing and debugging on physical hardware
Advanced Optimization Tactics
- Neural architecture search for TinyML
- Combined quantization and pruning approaches
- Model distillation for embedded inference
Recap and Future Steps
Requirements
- Knowledge of machine learning workflows
- Experience with embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals specializing in resource-constrained inference systems
