Asymmetrical Training Scheme of Binary-Memristor-Crossbar-Based Neural Networks for Energy-Efficient Edge-Computing Nanoscale Systems

Micromachines
Khoa Van PhamKyeong-Sik Min

Abstract

For realizing neural networks with binary memristor crossbars, memristors should be programmed by high-resistance state (HRS) and low-resistance state (LRS), according to the training algorithms like backpropagation. Unfortunately, it takes a very long time and consumes a large amount of power in training the memristor crossbar, because the program-verify scheme of memristor-programming is based on the incremental programming pulses, where many programming and verifying pulses are repeated until the target conductance. Thus, this reduces the programming time and power is very essential for energy-efficient and fast training of memristor networks. In this paper, we compared four different programming schemes, which are F-F, C-F, F-C, and C-C, respectively. C-C means both HRS and LRS are coarse-programmed. C-F has the coarse-programmed HRS and fine LRS, respectively. F-C is vice versa of C-F. In F-F, both HRS and LRS are fine-programmed. Comparing the error-energy products among the four schemes, C-F shows the minimum error with the minimum energy consumption. The asymmetrical coarse HRS and fine LRS can reduce the time and energy during the crossbar training significantly, because only LRS is fine-programmed. Moreover, the asymm...Continue Reading

References

May 3, 2008·Nature·Dmitri B StrukovR Stanley Williams
Mar 3, 2010·Nano Letters·Sung Hyun JoWei Lu
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Aug 5, 2015·Scientific Reports·Yansong GaoDerek Abbott
Jun 6, 2017·Nature Communications·Can LiQiangfei Xia
Nov 1, 2017·ACS Applied Materials & Interfaces·Sungjun KimByung-Gook Park

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