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Duration 21 hours
Course Outline
Fundamentals of Low-Power AI
- Overview of AI integration within embedded systems
- Addressing deployment challenges on resource-limited devices
- Exploring applications of energy-conscious AI
Strategies for Model Optimization
- Analyzing the effect of quantization on model performance
- Techniques in pruning and weight sharing
- Simplifying models through knowledge distillation
Deployment on Low-Power Hardware
- Leveraging TensorFlow Lite and ONNX Runtime for edge AI tasks
- Enhancing model efficiency using NVIDIA TensorRT
- Accelerating inference with Coral TPU and Jetson Nano
Minimizing Power Usage in AI Systems
- Conducting power profiling and assessing efficiency metrics
- Examining low-power computing architectures
- Applying dynamic power scaling and adaptive inference strategies
Practical Case Studies and Applications
- Implementing AI in battery-powered IoT ecosystems
- Utilizing low-power AI in healthcare and wearable technology
- Deploying AI for smart city and environmental monitoring solutions
Industry Best Practices and Emerging Trends
- Aligning edge AI strategies with sustainability goals
- Tracking advancements in energy-efficient AI hardware
- Anticipating future developments in low-power AI research
Recap and Recommended Next Steps
Requirements
- Foundational understanding of deep learning architectures
- Practical experience in embedded systems or AI deployment workflows
- Familiarity with standard model optimization strategies
Target Audience
- AI Engineers
- Embedded Developers
- Hardware Engineers
Testimonials (1)
That we can cover advance topic and work with real-life example
