Work

Analog, In-memory Compute Architectures for Artificial Intelligence

Year: 2023

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2302.06417

Authors: Bowen, Patrick, Regev, Guy, Regev, Nir, Pedroni, Bruno, Hanson, Edward, Chen, Yiran

Institutions: Duke University, Ben-Gurion University of the Negev, University of California San Diego

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4320854809

Cites:

Cited by: 2

Related to: 10