{"title":"AutoSA","authors":"Jie Wang, Licheng Guo, J. Cong","doi":"10.1145/3431920.3439292","DOIUrl":null,"url":null,"abstract":"While systolic array architectures have the potential to deliver tremendous performance, it is notoriously challenging to customize an efficient systolic array processor for a target application. Designing systolic arrays requires knowledge for both high-level characteristics of the application and low-level hardware details, thus making it a demanding and inefficient process. To relieve users from the manual iterative trial-and-error process, we present AutoSA, an end-to-end compilation framework for generating systolic arrays on FPGA. AutoSA is based on the polyhedral framework, and further incorporates a set of optimizations on different dimensions to boost performance. An efficient and comprehensive design space exploration is performed to search for high-performance designs. We have demonstrated AutoSA on a wide range of applications, on which AutoSA achieves high performance within a short amount of time. As an example, for matrix multiplication, AutoSA achieves 934 GFLOPs, 3.41 TOPs, and 6.95 TOPs in floating point, 16-bit and 8-bit integer data types on Xilinx Alveo U250.","PeriodicalId":386071,"journal":{"name":"The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3431920.3439292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
While systolic array architectures have the potential to deliver tremendous performance, it is notoriously challenging to customize an efficient systolic array processor for a target application. Designing systolic arrays requires knowledge for both high-level characteristics of the application and low-level hardware details, thus making it a demanding and inefficient process. To relieve users from the manual iterative trial-and-error process, we present AutoSA, an end-to-end compilation framework for generating systolic arrays on FPGA. AutoSA is based on the polyhedral framework, and further incorporates a set of optimizations on different dimensions to boost performance. An efficient and comprehensive design space exploration is performed to search for high-performance designs. We have demonstrated AutoSA on a wide range of applications, on which AutoSA achieves high performance within a short amount of time. As an example, for matrix multiplication, AutoSA achieves 934 GFLOPs, 3.41 TOPs, and 6.95 TOPs in floating point, 16-bit and 8-bit integer data types on Xilinx Alveo U250.