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Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

Engineering and Technology

Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

B. Alimkhanuly, J. Sohn, et al.

Discover how the innovative use of microfabricated, graphene-based Vertical RRAM (VRRAM) can revolutionize neuromorphic computing, enhancing energy efficiency and recognition accuracy. This exciting research conducted by Batyrbek Alimkhanuly, Joon Sohn, Ik-Joon Chang, and Seunghyun Lee showcases the advantages of graphene in advanced computing technologies.

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Playback language: English
Abstract
This work investigates the use of microfabricated, graphene-based Vertical RRAM (VRRAM) in neuromorphic computing. By using graphene, the VRRAM array shows improved read/write margins and reduced read inaccuracy, leading to significant energy reduction. An XNOR logic-inspired architecture with 1-bit ternary precision synaptic weights is introduced. Simulations show 83.5% and 94.1% recognition accuracy for VRRAMs with metal and graphene word-planes, respectively, highlighting the benefits of graphene.
Publisher
npj 2D Materials and Applications
Published On
May 14, 2021
Authors
Batyrbek Alimkhanuly, Joon Sohn, Ik-Joon Chang, Seunghyun Lee
Tags
Graphene
Vertical RRAM
Neuromorphic Computing
Energy Reduction
Recognition Accuracy
Synaptic Weights
XNOR Logic

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