A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System-Reference-Cited by-同舟云学术

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

Published:2022 Issue: Volume:10 Page:37030-37038
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Min-Hwi¹^ORCID,Lee Sin-Hyung²,Kim Sungjun³^ORCID,Park Byung-Gook¹^ORCID

Affiliation:

1. Inter-University of Semiconductor Research Center (ISRC) and Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea

2. School of Electronics Engineering, School of Electronic, and School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea

3. Division of Electronics and Electrical Engineering, Dongguk University, Seoul, South Korea

Funder

Korea Government (Ministry of Science and ICT) through the Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09729763.pdf?arnumber=9729763

Reference33 articles.

1. Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution

2. Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network Based on Analog Resistive Synapse