PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence

Saavan Patel, Philip Canoza, Adhiraj Datar, Steven Lu, Chirag Garg, Sayeef Salahuddin
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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.
PASS:面向下一代智能的异步概率处理器
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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