Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices

• An understanding of deep learning models
• Experience with embedded systems or AI deployment
• Basic knowledge of model optimization techniques

Audience

• AI engineers
• Embedded developers
• Hardware engineers

Low-power AI focuses on optimizing AI models to run efficiently on resource-constrained and battery-operated edge devices.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, embedded developers, and hardware engineers who wish to implement AI models on low-power devices while minimizing energy consumption.

By the end of this training, participants will be able to:

• Understand the challenges of running AI on energy-efficient devices.
• Optimize neural networks for low-power inference.
• Utilize quantization, pruning, and model compression techniques.
• Deploy AI models on edge hardware with minimal power usage.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Low-Power AI

• Overview of AI in embedded systems
• Challenges of AI deployment on low-power devices
• Energy-efficient AI applications

Model Optimization Techniques

• Quantization and its impact on performance
• Pruning and weight sharing
• Knowledge distillation for model simplification

Deploying AI Models on Low-Power Hardware

• Using TensorFlow Lite and ONNX Runtime for edge AI
• Optimizing AI models with NVIDIA TensorRT
• Hardware acceleration with Coral TPU and Jetson Nano

Reducing Power Consumption in AI Applications

• Power profiling and efficiency metrics
• Low-power computing architectures
• Dynamic power scaling and adaptive inference techniques

Case Studies and Real-World Applications

• AI-powered battery-operated IoT devices
• Low-power AI for healthcare and wearables
• Smart city and environmental monitoring applications

Best Practices and Future Trends

• Optimizing edge AI for sustainability
• Advancements in energy-efficient AI hardware
• Future developments in low-power AI research

Summary and Next Steps