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
 21 Hours

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