TY - GEN
T1 - Overcoming crossbar nonidealities in binary neural networks through learning
AU - Fouda, Mohammed E.
AU - Lee, Jongeun
AU - Eltawil, Ahmed M.
AU - Kurdahi, Fadi
N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20
PY - 2018/7/17
Y1 - 2018/7/17
N2 - The crossbar nonidealaties may considerably degrade the accuracy of matrix multiplication operation, which is the cornerstone of hardware accelerated neural networks. In this paper, we show that the crossbar nonidealities especially the wire resistance should be taken into consideration for accurate evaluation. We also present a simple yet highly effective way to capture the wire resistance effect for the inference and training of deep neural networks without extensive SPICE simulations. Different scenarios have been studied and used to show the efficacy of our proposed method.
AB - The crossbar nonidealaties may considerably degrade the accuracy of matrix multiplication operation, which is the cornerstone of hardware accelerated neural networks. In this paper, we show that the crossbar nonidealities especially the wire resistance should be taken into consideration for accurate evaluation. We also present a simple yet highly effective way to capture the wire resistance effect for the inference and training of deep neural networks without extensive SPICE simulations. Different scenarios have been studied and used to show the efficacy of our proposed method.
UR - http://dl.acm.org/citation.cfm?doid=3232195.3232226
UR - http://www.scopus.com/inward/record.url?scp=85060733229&partnerID=8YFLogxK
U2 - 10.1145/3232195.3232226
DO - 10.1145/3232195.3232226
M3 - Conference contribution
SN - 9781450358156
BT - Proceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2018
PB - Association for Computing Machinery, [email protected]
ER -