Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators
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Published:2024-01-02
Issue:1
Volume:15
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Jeon Kanghyeok, Ryu Jin Joo, Im Seongil, Seo Hyun Kyu, Eom Taeyong, Ju Hyunsu, Yang Min Kyu, Jeong Doo SeokORCID, Kim Gun HwanORCID
Abstract
AbstractMemristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.
Funder
Ministry of Culture, Sports and Tourism Korea Institute of Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
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