论文标题
优势可持续的AI处理
Sustainable AI Processing at the Edge
论文作者
论文摘要
边缘计算是加速机器学习算法支持移动设备的流行目标，而无需通信潜伏在云中。机器学习的边缘部署主要考虑传统问题，例如其安装的交换约束（尺寸，重量和功率）。但是，考虑到体现能量和碳的重要贡献，这种指标不足以考虑计算的环境影响。在本文中，我们探讨了用于推理和在线培训的卷积神经网络加速发动机的权衡。特别是，我们探讨了内存处理（PIM）方法，移动GPU加速器的使用以及最近发布的FPGA，并将其与新颖的赛道记忆PIM进行比较。用赛车记忆PIM替换支持PIM的DDR3可以恢复其体现的能量，尽快恢复1年。对于高活动比，与支持PIM的赛车记忆相比，移动GPU可以更可持续，但具有更高的体现能量可以克服。
Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud. Edge deployments of machine learning primarily consider traditional concerns such as SWaP constraints (Size, Weight, and Power) for their installations. However, such metrics are not entirely sufficient to consider environmental impacts from computing given the significant contributions from embodied energy and carbon. In this paper we explore the tradeoffs of convolutional neural network acceleration engines for both inference and on-line training. In particular, we explore the use of processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently released FPGAs, and compare them with novel Racetrack memory PIM. Replacing PIM-enabled DDR3 with Racetrack memory PIM can recover its embodied energy as quickly as 1 year. For high activity ratios, mobile GPUs can be more sustainable but have higher embodied energy to overcome compared to PIM-enabled Racetrack memory.
