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Condensed Matter > Mesoscale and Nanoscale Physics
Title: CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
(Submitted on 12 Apr 2023 (v1), last revised 23 Feb 2024 (this version, v3))
Abstract: Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.
Submission history
From: Kerem Çamsarı [view email][v1] Wed, 12 Apr 2023 16:18:12 GMT (13580kb,D)
[v2] Tue, 18 Apr 2023 03:10:53 GMT (27482kb,D)
[v3] Fri, 23 Feb 2024 05:04:33 GMT (15179kb,D)
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