papers AI Learner
The Github is limit! Click to go to the new site.

Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics

2017-03-30
Hyungjun Kim, Taesu Kim, Jinseok Kim, Jae-Joon Kim

Abstract

Artificial Neural Network computation relies on intensive vector-matrix multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array showed a feasibility of implementing such operations with high energy efficiency, thus there are many works on efficiently utilizing emerging NVM crossbar array as analog vector-matrix multiplier. However, its nonlinear I-V characteristics restrain critical design parameters, such as the read voltage and weight range, resulting in substantial accuracy loss. In this paper, instead of optimizing hardware parameters to a given neural network, we propose a methodology of reconstructing a neural network itself optimized to resistive memory crossbar arrays. To verify the validity of the proposed method, we simulated various neural network with MNIST and CIFAR-10 dataset using two different specific Resistive Random Access Memory (RRAM) model. Simulation results show that our proposed neural network produces significantly higher inference accuracies than conventional neural network when the synapse devices have nonlinear I-V characteristics.

Abstract (translated by Google)
URL

https://arxiv.org/abs/1703.10642

PDF

https://arxiv.org/pdf/1703.10642


Similar Posts

Comments