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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.10325 |
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| _version_ | 1866910605408993280 |
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| author | Patel, Saavan Canoza, Philip Datar, Adhiraj Lu, Steven Garg, Chirag Salahuddin, Sayeef |
| author_facet | Patel, Saavan Canoza, Philip Datar, Adhiraj Lu, Steven Garg, Chirag Salahuddin, Sayeef |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence Patel, Saavan Canoza, Philip Datar, Adhiraj Lu, Steven Garg, Chirag Salahuddin, Sayeef Distributed, Parallel, and Cluster Computing Hardware Architecture Emerging Technologies Data Analysis, Statistics and Probability 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. |
| title | PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture Emerging Technologies Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2409.10325 |