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
{"title":"A 7.3 M Output Non-Zeros/J Sparse Matrix-Matrix Multiplication Accelerator using Memory Reconfiguration in 40 nm","authors":"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","doi":"10.23919/VLSIT.2019.8776507","DOIUrl":null,"url":null,"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 $2.0\\ \\text{mm}\\times 2.6\\ \\text{mm}$ 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.","PeriodicalId":6752,"journal":{"name":"2019 Symposium on VLSI Technology","volume":"166 9 Suppl 1","pages":"C150-C151"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIT.2019.8776507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
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 $2.0\ \text{mm}\times 2.6\ \text{mm}$ 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.