AI Models in Edge Computing: Performance and Optimization

AI Models in Edge Computing

Introduction

As AI becomes more prevalent, the need to run models on edge devices grows. This research explores optimization techniques and performance considerations for edge AI deployment.

Current Progress

Our investigation covers:

• Model optimization techniques
• Hardware acceleration options
• Power consumption analysis
• Real-time processing capabilities

Preliminary Findings

1. Model Optimization

   • Quantization reduces model size by up to 75%
   • Pruning improves inference speed by 40%
   • Minimal accuracy loss (1-2%)

2. Hardware Acceleration

   • Neural Processing Units (NPUs) show 3x performance improvement
   • GPU acceleration viable for specific architectures
   • CPU-only solutions remain relevant for basic models

3. Power Efficiency

   • Optimized models reduce power consumption by 60%
   • Batch processing improves energy efficiency
   • Hardware-specific optimizations crucial

Ongoing Research

Current focus areas:

• Model architecture optimization
• Custom hardware acceleration
• Battery life optimization
• Real-world deployment scenarios

Next Steps

Planned investigations:

• Advanced quantization techniques
• Multi-device coordination
• Privacy-preserving inference
• Automated optimization pipelines