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Course Outline
Fundamentals of Low-Power AI
- Role of AI within embedded systems
- Key challenges in deploying AI on low-power platforms
- Examples of energy-efficient AI applications
Strategies for Model Optimization
- Effects of quantization on model performance
- Techniques for pruning and weight sharing
- Simplifying models through knowledge distillation
Implementing AI on Low-Power Hardware
- Utilizing TensorFlow Lite and ONNX Runtime for edge inference
- Enhancing AI model performance with NVIDIA TensorRT
- Leveraging hardware acceleration via Coral TPU and Jetson Nano
Minimizing Power Consumption in AI Workflows
- Conducting power profiling and analyzing efficiency metrics
- Understanding low-power computing architectures
- Applying dynamic power scaling and adaptive inference methods
Practical Case Studies and Industry Applications
- AI integration in battery-operated IoT devices
- Low-power AI solutions for healthcare and wearable technology
- Applications in smart city infrastructure and environmental monitoring
Industry Best Practices and Emerging Trends
- Focusing edge AI strategies on sustainability
- Recent advancements in energy-efficient AI hardware
- Future directions in low-power AI research and development
Course Wrap-up and Recommended Next Steps
Requirements
- Foundational knowledge of deep learning architectures
- Prior experience with embedded systems or AI deployment workflows
- Basic familiarity with model optimization methodologies
Target Audience
- AI engineers
- Embedded developers
- Hardware engineers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
