Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to TinyML and Embedded AI
- Key aspects of TinyML model deployment
- Limitations within microcontroller environments
- Introduction to embedded AI toolchains
Foundations of Model Optimization
- Analyzing computational bottlenecks
- Recognizing memory-intensive operations
- Establishing baseline performance profiles
Quantization Methods
- Post-training quantization approaches
- Quantization-aware training techniques
- Assessing the balance between accuracy and resource usage
Pruning and Compression
- Structured and unstructured pruning strategies
- 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 Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Testing for 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 approaches
- Using model distillation for embedded inference
Conclusion and Future Steps
Requirements
- A solid grasp of machine learning workflows
- Practical experience with embedded systems or microcontroller-based development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Professionals developing inference systems under resource constraints
21 Hours
