Energy-Aware Optimization Techniques for Machine Learning Hardware

Abstract

The proliferation of machine learning (ML) applications has driven the rapid evolution of hardware systems tailored for efficient computation. However, this progress has come with significant energy demands, posing environmental challenges and operational costs. Energy-aware optimization techniques for ML hardware have emerged as a critical area of research to address these challenges. This paper explores innovative methods, including hardware-specific optimizations, algorithmic adjustments, and architectural improvements, that reduce energy consumption without compromising computational accuracy or speed. By integrating energy-efficient principles into ML workflows, these techniques ensure sustainable advancements in artificial intelligence.