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.
|Original language||English (US)|
|Title of host publication||Proceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2018|
|Publisher||Association for Computing Machinery, Incacmhelp@acm.org|
|State||Published - Jul 17 2018|