Demonstration of transfer learning using 14 nm technology analog ReRAM array

Author:

Athena Fabia Farlin,Fagbohungbe Omobayode,Gong Nanbo,Rasch Malte J.,Penaloza Jimmy,Seo SoonCheon,Gasasira Arthur,Solomon Paul,Bragaglia Valeria,Consiglio Steven,Higuchi Hisashi,Park Chanro,Brew Kevin,Jamison Paul,Catano Christopher,Saraf Iqbal,Silvestre Claire,Liu Xuefeng,Khan Babar,Jain Nikhil,McDermott Steven,Johnson Rick,Estrada-Raygoza I.,Li Juntao,Gokmen Tayfun,Li Ning,Pujari Ruturaj,Carta Fabio,Miyazoe Hiroyuki,Frank Martin M.,La Porta Antonio,Koty Devi,Yang Qingyun,Clark Robert D.,Tapily Kandabara,Wajda Cory,Mosden Aelan,Shearer Jeff,Metz Andrew,Teehan Sean,Saulnier Nicole,Offrein Bert,Tsunomura Takaaki,Leusink Gert,Narayanan Vijay,Ando Takashi

Abstract

Analog memory presents a promising solution in the face of the growing demand for energy-efficient artificial intelligence (AI) at the edge. In this study, we demonstrate efficient deep neural network transfer learning utilizing hardware and algorithm co-optimization in an analog resistive random-access memory (ReRAM) array. For the first time, we illustrate that in open-loop deep neural network (DNN) transfer learning for image classification tasks, convergence rates can be accelerated by approximately 3.5 times through the utilization of co-optimized analog ReRAM hardware and the hardware-aware Tiki-Taka v2 (TTv2) algorithm. A simulation based on statistical 14 nm CMOS ReRAM array data provides insights into the performance of transfer learning on larger network workloads, exhibiting notable improvement over conventional training with random initialization. This study shows that analog DNN transfer learning using an optimized ReRAM array can achieve faster convergence with a smaller dataset compared to training from scratch, thus augmenting AI capability at the edge.

Publisher

Frontiers Media SA

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3