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This paper investigates hardware-software co-design techniques to improve energy efficiency in large-scale deep learning training on NVIDIA, AMD, and emerging GPU architectures. It focuses on memory-level and kernel-level optimizations, including specialized tensor cores, memory optimization methods, mixed-precision arithmetic, and energy-aware scheduling. The study demonstrates that co-design can significantly improve training efficiency and reduce the carbon footprint of AI, supported by case studies from companies like Meta, Google, and Amazon.
Dramatically slash the carbon footprint of AI training without sacrificing performance by co-designing hardware and software for modern GPUs.
In particular, large-scale deep learning and artificial intelligence model training uses a lot of computational power and energy, so it poses serious sustainability issues. The fast rise in model complexity has resulted in exponential increases in energy consumption, increasing the demand for techniques maximizing computational efficiency and lowering environmental impact. This work explores environmentally driven performance optimization methods especially intended for advanced GPU architectures from NVIDIA, AMD, and other emerging GPU architectures. Our main focus is on investigating hardware-software co-design techniques meant to significantly increase memorylevel and kernel-level operations, so improving performance-perwatt measures. Our thorough research encompasses evaluations of specialized tensor and matrix cores, advanced memory optimization methods, and creative integration approaches that taken together result in notable energy efficiency increases. We also discuss important software-level optimizations that augment hardware capability including mixed-precision arithmetic, advanced energy-aware scheduling algorithms, and compilerdriven kernel enhancements. Moreover, we methodically point out important research gaps and suggest future directions necessary to create really sustainable artificial intelligence systems. This paper emphasizes how major increases in training efficiency can be obtained by co-design of hardware and software, so lowering the environmental impact of artificial intelligence without compromising performance. To back up our analysis, we use real-world case studies from top companies like Meta, Google, Amazon, and others that show how these sustainable AI training methods are used in the real world. With this thorough analysis, we show that a comprehensive co-design approach can significantly increase training efficiency and lower the carbon footprint of AI without compromising performance.
Amazon Science
Google Research
Meta AI