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

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

SUBMITTER: Jeon K 

PROVIDER: S-EPMC10761713 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature