Analog, In-memory Compute Architectures for Artificial Intelligence

2023/01/13 by Patrick Bowen, Bowen, Patrick, Guy Regev +9 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Hardware Architecture (cs.AR) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Optics (physics.optics)

paper · pdf · doi:10.48550/arxiv.2302.06417

openalex publication_date 2023/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference. The focus of the analysis is on how efficiency scales with the size, arithmetic intensity, and bit precision of the computation to be performed. It is shown that analog, in-memory computing architectures can approach arbitrarily high energy efficiency as both the problem size and processor size scales.

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