Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Kanghyeok Jeon, Jin Joo Ryu, Seongil Im, Hyun Kyu Seo, Taeyong Eom, Hyunsu Ju (), Min Kyu Yang (), Doo Seok Jeong () and Gun Hwan Kim ()
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Kanghyeok Jeon: Hanyang University
Jin Joo Ryu: Korea Research Institute of Chemical Technology (KRICT)
Seongil Im: Korea Institute of Science and Technology (KIST)
Hyun Kyu Seo: Sahmyook University
Taeyong Eom: Korea Research Institute of Chemical Technology (KRICT)
Hyunsu Ju: Korea Institute of Science and Technology (KIST)
Min Kyu Yang: Sahmyook University
Doo Seok Jeong: Hanyang University
Gun Hwan Kim: Yonsei University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Memristor-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.

Date: 2024
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DOI: 10.1038/s41467-023-44620-1

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