stochastic computing (SC)

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Probabilistic Computing With Neuromorphic Elements

Probabilistic Computing With Neuromorphic Elements 150 150
Author(s): Zonglong Li, Laurie E. Calvet

Abstract:

This article presents a compact and low-power approach to probabilistic inference based on time-domain analog computation. The proposed system implements a Bayesian classifier using neuromorphic circuit elements simulated in a 130-nm CMOS technology. Probabilities are encoded in the duty cycle of deterministic spiking signals generated by neuron circuits, while likelihood …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 167 – 175
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3712198
Publisher: IEEE

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OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication

OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication 150 150
Author(s): Shady Agwa, Yihan Pan, Georgios Papandroulidakis, Themis Prodromakis

Abstract:

Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computational bottleneck. In-memory computing (IMC) architectures are proposed to avoid the von Neumann bottleneck. However, both digital/binary-based and analog IMC architectures suffer from various limitations, which significantly degrade …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 56 – 64
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3680281
Publisher: IEEE

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