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

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: 15

摘要

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。$2.0\ \text{mm}\times 2.6\ \text{mm}$芯片显示出$12.6\倍(8.4\倍)$能源效率增益,$11.7\倍(77.6\倍)$片外带宽效率增益和$17.1\倍(36.9\倍)$计算密度增益,与高端CPU (GPU)相比,跨一组不同的合成和基于真实世界幂律图的稀疏矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 7.3 M Output Non-Zeros/J Sparse Matrix-Matrix Multiplication Accelerator using Memory Reconfiguration in 40 nm
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 $2.0\ \text{mm}\times 2.6\ \text{mm}$ 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.
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