Richard Haight, Talia S. Gershon, et al.
Semiconductor Science and Technology
Resistive crossbar arrays are promising options for accelerating enormous computation needed for training modern deep neural networks (DNNs). However, verification of this idea has not been scaled up to realistically sized DNNs due to the nonideal device behavior and hardware design constraints. In this article, the authors propose a novel simulation framework to explore such design constraints on the large-scale problems and devise algorithmic measures to pave the way for robust resistive crossbar-based DNN training accelerators. - Jungwook Choi, IBM Research.
Richard Haight, Talia S. Gershon, et al.
Semiconductor Science and Technology
Kuan-Neng Chen, Sang Hwui Lee, et al.
IEDM 2006
Aaron D. Franklin, Shu-Jen Han, et al.
IEDM 2011
Wilfried Haensch, Tayfun Gokmen, et al.
Proceedings of the IEEE