{"id":132015,"date":"2022-07-25T18:51:33","date_gmt":"2022-07-25T09:51:33","guid":{"rendered":"http:\/\/192.249.19.202\/?post_type=ai-in-circuit&#038;p=132015"},"modified":"2026-04-05T18:04:48","modified_gmt":"2026-04-05T09:04:48","slug":"fault-free-a-fault-resilient-deep-neural-network-accelerator-based-on-realistic-reram-devices","status":"publish","type":"ai-in-circuit","link":"http:\/\/ee.presscat.kr\/en\/ai-in-circuit\/fault-free-a-fault-resilient-deep-neural-network-accelerator-based-on-realistic-reram-devices\/","title":{"rendered":"Fault-free: A Fault-resilient Deep Neural Network Accelerator based on Realistic ReRAM Devices"},"content":{"rendered":"<p>Title : Fault-free: A Fault-resilient Deep Neural Network Accelerator based on Realistic ReRAM Devices<\/p>\n<p>&nbsp;<\/p>\n<p>Author: Hyein Shin, Myeonggu Kang, Lee-Sup Kim<\/p>\n<p>&nbsp;<\/p>\n<p>Conference: IEEE\/ACM Design Automation Conference (DAC) 2021<\/p>\n<p>&nbsp;<\/p>\n<p>Abstract: Energy-efficient Resistive RAM (ReRAM) based deep neural network (DNN) accelerator suffers from severe Stuck-At-Fault (SAF) problem that drastically degrades the inference accuracy. The SAF problem gets even worse in realistic ReRAM devices with low cell resolution. To address the issue, we propose a fault-resilient DNN accelerator based on realistic ReRAM devices. We first analyze the SAF problem in a realistic ReRAM device and propose a 3-stage offline fault-resilient compilation and lightweight online compensation. The proposed work enables the reliable execution of DNN with only 5% area and 0.8% energy overhead from the ideal ReRAM-based DNN accelerator.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-132016\" src=\"http:\/\/ee.presscat.kr\/wp-content\/uploads\/2022\/07\/\uae40\uc774\uc12d7.jpg\" alt=\"\" width=\"1430\" height=\"593\" title=\"\" srcset=\"http:\/\/ee.presscat.kr\/wp-content\/uploads\/2022\/07\/\uae40\uc774\uc12d7.jpg 1430w, http:\/\/ee.presscat.kr\/wp-content\/uploads\/2022\/07\/\uae40\uc774\uc12d7-300x124.jpg 300w, http:\/\/ee.presscat.kr\/wp-content\/uploads\/2022\/07\/\uae40\uc774\uc12d7-1024x425.jpg 1024w, http:\/\/ee.presscat.kr\/wp-content\/uploads\/2022\/07\/\uae40\uc774\uc12d7-768x318.jpg 768w\" sizes=\"(max-width: 1430px) 100vw, 1430px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>795<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-132015","ai-in-circuit","type-ai-in-circuit","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-circuit\/132015","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-circuit"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-circuit"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=132015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}