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Course Outline
Introduction to Low-Power AI
- Exploring the landscape of AI within embedded systems.
- Addressing the specific hurdles of AI deployment on low-power devices.
- Examining practical applications of energy-efficient AI.
Advanced Model Optimization Techniques
- Analyzing the trade-offs between quantization and model performance.
- Implementing pruning strategies and weight sharing methods.
- Applying knowledge distillation to simplify and streamline models.
Deploying AI Models on Low-Power Hardware
- Utilizing TensorFlow Lite and ONNX Runtime for seamless edge AI integration.
- Enhancing model efficiency using NVIDIA TensorRT.
- Leveraging hardware acceleration through Coral TPU and Jetson Nano.
Minimizing Power Consumption in AI Applications
- Conducting thorough power profiling and evaluating efficiency metrics.
- Understanding low-power computing architectures and their benefits.
- Implementing dynamic power scaling and adaptive inference strategies.
Case Studies and Real-World Implementations
- Examining AI-driven solutions for battery-operated IoT devices.
- Exploring low-power AI applications in healthcare and wearable technology.
- Reviewing smart city infrastructure and environmental monitoring systems.
Best Practices and Emerging Trends
- Aligning edge AI optimization with sustainability goals.
- Tracking recent advancements in energy-efficient AI hardware.
- Predicting future trajectories in low-power AI research and development.
Summary and Strategic Next Steps
Requirements
- A solid grasp of deep learning model architectures.
- Practical experience with embedded systems or AI deployment pipelines.
- Foundational knowledge of standard model optimization techniques.
Target Audience
- AI Engineers
- Embedded Developers
- Hardware Engineers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
