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Efficient Sneak Path-aware Training of Binarized Neural Networks for RRAM Crossbar Arrays

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Title
Efficient Sneak Path-aware Training of Binarized Neural Networks for RRAM Crossbar Arrays
Author
Lee, Sugil
Advisor
Lee, Jongeun
Issue Date
2019-08
Publisher
Graduate School of UNIST
Abstract
Although RRAM crossbar arrays have been suggested as an efficient way to implement MVM for DNNS, the sneak path problem of RRAM crossbar arrays due to wire resistance can distort the result of MVM quite significantly, resulting harsh performance degradation of the network. Therefore, a software solution that can predict the effect of sneak paths to mitigate the impact without permanent hardware cost or expensive SPICE simulations is very desirable. In this paper, a novel method to incorporate the sneak path problem during training with a negligible overhead is proposed. The test validation results, done through accurate SPICE simulations, show very high improvement in the performance close to the baseline BNNs on GPU, which demonstrates the efficiency of the proposed method to capture the sneak path problem.
Description
Department of Computer Science and Engineering
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EE_Theses_Master
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