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/VLSIC.2019.8778147","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 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.","PeriodicalId":6707,"journal":{"name":"2019 Symposium on VLSI Circuits","volume":"19 1","pages":"C150-C151"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIC.2019.8778147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.