A 7.3 M Output Non-Zeros/J Sparse Matrix-Matrix Multiplication Accelerator using Memory Reconfiguration in 40 nm

S. Pal, Dong-hyeon Park, Siying Feng, Paul Gao, Jielun Tan, A. Rovinski, Shaolin Xie, Chun Zhao, Aporva Amarnath, Tim Wesley, Jonathan Beaumont, Kuan-Yu Chen, C. Chakrabarti, M. Taylor, T. Mudge, D. Blaauw, Hun-Seok Kim, R. Dreslinski
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引用次数: 1

Abstract

A Sparse Matrix-Matrix multiplication (SpMM) accelerator with 48 heterogeneous cores and a reconfigurable memory hierarchy is fabricated in 40 nm CMOS. On-chip memories are reconfigured as scratchpad or cache and interconnected with synthesizable coalescing crossbars for efficient memory access in each phase of the algorithm. The 2.0 mm $ \times 2.6$ mm chip exhibits $12.6 \times (8.4\times)$ energy efficiency gain, $11.7\times (77.6\times)$ off-chip bandwidth efficiency gain and$17.1\times (36.9\times)$ compute density gain against a high-end CPU (GPU) across a diverse set of synthetic and real-world power-law graph based sparse matrices.
基于40 nm内存重构的7.3 M输出非零/J稀疏矩阵-矩阵乘法加速器
采用40 nm CMOS工艺,研制了具有48个异构核和可重构存储器结构的稀疏矩阵-矩阵乘法(SpMM)加速器。片上存储器被重新配置为刮擦板或缓存,并与可合成的聚结交叉条互连,以便在算法的每个阶段有效地访问存储器。2.0 mm × 2.6 mm的芯片在不同的合成和基于真实世界幂律图的稀疏矩阵中,与高端CPU (GPU)相比,具有12.6倍(8.4倍)的能效增益,11.7倍(77.6倍)的片外带宽效率增益和17.1倍(36.9倍)的计算密度增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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