Jeon, Kanghyeok Ryu, Jin Joo Im, Seongil Seo, Hyun Kyu Eom, Taeyong Ju, Hyunsu Yang, Min Kyu Jeong, Doo Seok Kim, Gun Hwan 2024-04-04T02:02:09Z 2024-04-04T02:02:09Z 2024-04-04 2024-01 https://pubs.kist.re.kr/handle/201004/149572 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. English Nature Publishing Group Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators Article 10.1038/s41467-023-44620-1 1 Nature Communications, v.15, no.1 Nature Communications 15 1 Y scie scopus 001158425400087 2-s2.0-85181243029 Multidisciplinary Sciences Science & Technology - Other Topics Article MEMORY