Energy-Efficient Deep Learning: A Comprehensive Review
This comprehensive review examines state-of-the-art approaches for making deep learning more energy-efficient across the entire stack, from hardware to algorithms. The research analyzes various efficiency techniques including model compression, neural architecture search, and hardware-software co-design for energy-efficient deep learning. The authors provide detailed case studies and empirical evaluations of different approaches, offering insights into their effectiveness for reducing energy consumption while maintaining model performance.