Saavan Patel, Philip Canoza, Adhiraj Datar, Steven Lu, Chirag Garg, Sayeef Salahuddin
{"title":"PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence","authors":"Saavan Patel, Philip Canoza, Adhiraj Datar, Steven Lu, Chirag Garg, Sayeef Salahuddin","doi":"arxiv-2409.10325","DOIUrl":null,"url":null,"abstract":"New computing paradigms are required to solve the most challenging\ncomputational problems where no exact polynomial time solution\nexists.Probabilistic Ising Accelerators has gained promise on these problems\nwith the ability to model complex probability distributions and find ground\nstates of intractable problems. In this context, we have demonstrated the\nParallel Asynchronous Stochastic Sampler (PASS), the first fully on-chip\nintegrated, asynchronous, probabilistic accelerator that takes advantage of the\nintrinsic fine-grained parallelism of the Ising Model and built in state of the\nart 14nm CMOS FinFET technology. We have demonstrated broad applicability of\nthis accelerator on problems ranging from Combinatorial Optimization, Neural\nSimulation, to Machine Learning along with up to $23,000$x energy to solution\nimprovement compared to CPUs on probabilistic problems.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"54 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Data Analysis, Statistics and Probability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
New computing paradigms are required to solve the most challenging
computational problems where no exact polynomial time solution
exists.Probabilistic Ising Accelerators has gained promise on these problems
with the ability to model complex probability distributions and find ground
states of intractable problems. In this context, we have demonstrated the
Parallel Asynchronous Stochastic Sampler (PASS), the first fully on-chip
integrated, asynchronous, probabilistic accelerator that takes advantage of the
intrinsic fine-grained parallelism of the Ising Model and built in state of the
art 14nm CMOS FinFET technology. We have demonstrated broad applicability of
this accelerator on problems ranging from Combinatorial Optimization, Neural
Simulation, to Machine Learning along with up to $23,000$x energy to solution
improvement compared to CPUs on probabilistic problems.