Neural Networks

This comprehensive study analyzes the real carbon footprint of training large neural network models, taking into account multiple often-overlooked factors. The research provides a detailed methodology for calculating CO2 emissions and demonstrates how the choice of data center location and timing can significantly impact the environmental cost of AI training. The authors show that thoughtful choices about where and when to train models can reduce CO2 emissions by up to 100x compared to random choices.

This comprehensive survey examines various approaches to making transformer models more computationally efficient and environmentally sustainable. The research analyzes different architectural innovations and training strategies that reduce the computational and energy requirements of transformer models while maintaining their effectiveness. The authors provide a systematic comparison of different efficiency techniques and their impact on model performance, training costs, and environmental footprint.

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.