Saavan Patel, Philip Canoza, Adhiraj Datar, Steven Lu, Chirag Garg, Sayeef Salahuddin
{"title":"PASS:面向下一代智能的异步概率处理器","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":"{\"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}","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
摘要
要解决没有精确多项式时间解决方案的最具挑战性的计算问题,就必须采用新的计算范式。概率伊辛加速器能够模拟复杂的概率分布并找到棘手问题的基态,因此在这些问题上大有可为。在此背景下,我们展示了并行异步随机取样器 (PASS),这是首个完全集成在芯片上的异步概率加速器,它利用了伊辛模型内在的细粒度并行性,并采用最先进的 14nm CMOS FinFET 技术。我们已经证明了该加速器在组合优化、神经仿真和机器学习等问题上的广泛适用性,与 CPU 相比,在概率问题上的能效提高了 23,000 美元。
PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence
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.