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

• Published in: NPJ 2D materials and applications Vol. 5; no. 1; pp. 1 - 10
• Main Authors: Alimkhanuly, Batyrbek, Sohn, Joon, Chang, Ik-Joon, Lee, Seunghyun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.05.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1005/1007; 639/705; 639/925/918/1052; 639/925/927/1007; Arrays; Chemistry and Materials Science; Circuits; Computation; Design optimization; Energy consumption; Graphene; Materials Science; Materials selection; Nanotechnology; Neural networks; Neuromorphic computing; Surfaces and Interfaces; Thin Films; Two dimensional materials

Recent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual...