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.
Duration 21 hours
Course Outline
Introduction to TinyML and Embedded AI
- Key aspects of deploying TinyML models
- Limits within microcontroller environments
- Overview of available embedded AI toolchains
Foundations of Model Optimization
- Identifying computational bottlenecks
- Recognizing memory-intensive operations
- Establishing baseline performance profiles
Quantization Methods
- Strategies for post-training quantization
- Implementing quantization-aware training
- Balancing accuracy against resource usage
Pruning and Compression
- Techniques for structured and unstructured pruning
- Utilizing weight sharing and model sparsity
- Applying compression algorithms for lightweight inference
Hardware-Specific Optimization
- Deploying models on ARM Cortex-M architectures
- Tuning for DSP and accelerator extensions
- Considering memory mapping and dataflow efficiency
Benchmarking and Validation
- Analyzing latency and throughput
- Measuring power and energy consumption
- Conducting accuracy and robustness tests
Deployment Workflows and Tooling
- Leveraging TensorFlow Lite Micro for embedded applications
- Integrating TinyML models with Edge Impulse pipelines
- Performing testing and debugging on physical hardware
Advanced Optimization Strategies
- Applying neural architecture search to TinyML
- Combining quantization and pruning techniques
- Using model distillation for embedded inference
Conclusion and Future Steps
Requirements
- Comprehensive understanding of machine learning workflows
- Hands-on experience with embedded systems or microcontroller development
- Proficiency in Python programming
Target Audience
- AI researchers
- Embedded ML engineers
- Specialists working on inference systems with resource constraints
