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Demonstration of transfer learning using 14 nm technology analog ReRAM array

Authors :
Fabia Farlin Athena
Omobayode Fagbohungbe
Nanbo Gong
Malte J. Rasch
Jimmy Penaloza
SoonCheon Seo
Arthur Gasasira
Paul Solomon
Valeria Bragaglia
Steven Consiglio
Hisashi Higuchi
Chanro Park
Kevin Brew
Paul Jamison
Christopher Catano
Iqbal Saraf
Claire Silvestre
Xuefeng Liu
Babar Khan
Nikhil Jain
Steven McDermott
Rick Johnson
I. Estrada-Raygoza
Juntao Li
Tayfun Gokmen
Ning Li
Ruturaj Pujari
Fabio Carta
Hiroyuki Miyazoe
Martin M. Frank
Antonio La Porta
Devi Koty
Qingyun Yang
Robert D. Clark
Kandabara Tapily
Cory Wajda
Aelan Mosden
Jeff Shearer
Andrew Metz
Sean Teehan
Nicole Saulnier
Bert Offrein
Takaaki Tsunomura
Gert Leusink
Vijay Narayanan
Takashi Ando
Source :
Frontiers in Electronics, Vol 4 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

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.

Details

Language :
English
ISSN :
26735857 and 83908250
Volume :
4
Database :
Directory of Open Access Journals
Journal :
Frontiers in Electronics
Publication Type :
Academic Journal
Accession number :
edsdoj.251ccb96d4dd435e83908250513bf0fa
Document Type :
article
Full Text :
https://doi.org/10.3389/felec.2023.1331280