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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 available embedded AI toolchains
Foundations of Model Optimization
- Recognizing computational bottlenecks
- Identifying memory-heavy operations
- Establishing baseline performance profiles
Quantization Methods
- Post-training quantization approaches
- Quantization-aware training processes
- Assessing the trade-off between accuracy and resource usage
Pruning and Compression
- Structured versus unstructured pruning techniques
- Weight sharing and model sparsity concepts
- Compression algorithms for lightweight inference
Hardware-Centric Optimization
- Deploying models on ARM Cortex-M systems
- Optimizing for DSP and accelerator extensions
- Considerations for memory mapping and dataflow
Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing for accuracy and robustness
Deployment Workflows and Tools
- Leveraging 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 approaches
- Using model distillation for embedded inference
Summary and Next Steps
Requirements
- A solid grasp 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
Testimonials (1)
The exercises
