tuGEMM:用于低精度边缘人工智能的面积-功率效率时间一元GEMM架构

Harideep Nair, P. Vellaisamy, Albert Chen, Joseph Finn, Anna Li, Manav Trivedi, J. Shen
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引用次数: 0

摘要

通用矩阵乘法(GEMM)是一种无处不在的计算内核/算法,用于各种应用的数据处理,包括人工智能(AI)和深度学习(DL)。最近向边缘计算的转变激发了基于一元计算的GEMM架构,这主要是随机和速率编码系统。本文提出了一种新的基于时间编码的GEMM架构,称为tuGEMM,它可以进行精确计算。我们介绍了tuGEMM的两种变体,串行和并行,具有不同的面积/功率延迟权衡。报道了45纳米CMOS合成后的功率性能面积(PPA),用于2位,4位和8位计算。与最先进的随机一元系统相比,该设计在面积功率效率方面具有显着优势,特别是在低精度下,例如,4位仅产生0.03 mm2和9 mW, 2位仅产生0.01 mm2和4 mW。这使得tuGEMM非常适合功率受限的移动和边缘设备执行始终在线的实时传感处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI
General matrix multiplication (GEMM) is a ubiqui-tous computing kernel/algorithm for data processing in diverse applications, including artificial intelligence (AI) and deep learning (DL). Recent shift towards edge computing has inspired GEMM architectures based on unary computing, which are predominantly stochastic and rate-coded systems. This paper proposes a novel GEMM architecture based on temporal-coding, called tuGEMM, that performs exact computation. We introduce two variants of tuGEMM, serial and parallel, with distinct area/power-latency trade-offs. Post-synthesis Power-Performance-Area (PPA) in 45 nm CMOS are reported for 2-bit, 4-bit, and 8-bit computations. The designs illustrate significant advantages in area-power efficiency over state-of-the-art stochastic unary systems especially at low precisions, e.g. incurring just 0.03 mm2 and 9 mW for 4 bits, and 0.01 mm2 and 4 mW for 2 bits. This makes tuGEMM ideal for power constrained mobile and edge devices performing always-on real-time sensory processing.
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