Low-Power AI: Optimizing Edge AI for Energy-Efficient Devices Training Course
Low-power AI focuses on optimizing artificial intelligence models to run efficiently on resource-constrained and battery-operated edge devices.
This instructor-led, live training (online or onsite) is designed for advanced-level AI engineers, embedded developers, and hardware engineers who aim 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 associated with running AI on energy-efficient devices.
- Optimize neural networks for efficient low-power inference.
- Utilize techniques such as quantization, pruning, and model compression.
- Deploy AI models on edge hardware with minimal power usage.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for government, please contact us to arrange.
Course Outline
Introduction to Low-Power AI for Government
- Overview of Artificial Intelligence (AI) in embedded systems
- Challenges of deploying AI on low-power devices
- Energy-efficient AI applications for government use
Model Optimization Techniques
- Quantization and its impact on performance in public sector applications
- Pruning and weight sharing methods to enhance efficiency
- Knowledge distillation for simplifying models while maintaining accuracy
Deploying AI Models on Low-Power Hardware for Government
- Utilizing TensorFlow Lite and ONNX Runtime for edge AI in government systems
- Optimizing AI models with NVIDIA TensorRT for enhanced performance
- Leveraging hardware acceleration with Coral TPU and Jetson Nano for government applications
Reducing Power Consumption in AI Applications for Government
- Power profiling and efficiency metrics to ensure sustainable operations
- Low-power computing architectures tailored for public sector needs
- Dynamic power scaling and adaptive inference techniques for efficient resource management
Case Studies and Real-World Applications for Government
- AI-powered battery-operated IoT devices for government use
- Low-power AI solutions for healthcare and wearable technology in the public sector
- Smart city and environmental monitoring applications for enhanced governance
Best Practices and Future Trends for Government
- Optimizing edge AI to support sustainability goals in government operations
- Advancements in energy-efficient AI hardware for public sector deployment
- Future developments in low-power AI research and their implications for government
Summary and Next Steps for Government
Requirements
- A comprehensive understanding of deep learning models for government applications
- Practical experience with embedded systems or the deployment of artificial intelligence solutions
- Fundamental knowledge of model optimization techniques to enhance performance in public sector environments
Audience
- AI engineers for government projects
- Embedded developers for government systems
- Hardware engineers supporting government initiatives
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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