Gaspar Tognetti, Jonah P. Sengupta, P. Pouliquen, A. Andreou
{"title":"Characterization of a pseudo-DRAM Crossbar Computational Memory Array in 55nm CMOS : (Invited Paper)","authors":"Gaspar Tognetti, Jonah P. Sengupta, P. Pouliquen, A. Andreou","doi":"10.1109/CISS.2019.8692863","DOIUrl":null,"url":null,"abstract":"As computational needs increase in relation to the growing fields of Internet of Things and Deep Learning, energy-efficient, computational units are needed to bypass DSP units within Von Neumann architectures. A charge-mode vector matrix multiplier (VMM) with compute-in memory capabilities was fabricated in the Global Foundries 55nm LP process. The array is comprised of a 156 row by 512 column crossbar where each row computes a 512 element binary dot product in the charge domain. This normalized analog multiply and accumulate (MAC) is carried out by charge-injection devices who compute a 1-bit multiplication in the charge domain. Preliminary test results show successful, linear output computation in the analog domain to various input vectors that are both digital and multi-level analog. The 156 × 512 compute-in memory, CID array has been simulated to achieve an efficiency of 1.8 TOPs per mW.","PeriodicalId":123696,"journal":{"name":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 53rd Annual Conference on Information Sciences and Systems (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2019.8692863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
As computational needs increase in relation to the growing fields of Internet of Things and Deep Learning, energy-efficient, computational units are needed to bypass DSP units within Von Neumann architectures. A charge-mode vector matrix multiplier (VMM) with compute-in memory capabilities was fabricated in the Global Foundries 55nm LP process. The array is comprised of a 156 row by 512 column crossbar where each row computes a 512 element binary dot product in the charge domain. This normalized analog multiply and accumulate (MAC) is carried out by charge-injection devices who compute a 1-bit multiplication in the charge domain. Preliminary test results show successful, linear output computation in the analog domain to various input vectors that are both digital and multi-level analog. The 156 × 512 compute-in memory, CID array has been simulated to achieve an efficiency of 1.8 TOPs per mW.