Two-dimensional materials for artificial synapses: toward a practical application. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Wang, I-Ting; Chang, Chih-Cheng; Chen, Yen-Yu; Su, Yi-Shin; Hou, Tuo-Hung
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: 2D material -- synapse -- neuromorphic computing -- deep neural network -- hardware implementation; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac5086
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Combining the emerging two-dimensional materials (2DMs) and neuromorphic computing, 2DM-based synaptic devices (2DM synapse) are highly anticipated research topics with the promise of revolutionizing the present Si-based computing paradigm. Although the development is still in the early stage, the number of 2DM synapses reported has increased exponentially in the past few years. Nevertheless, most of them mainly focus on device-level synaptic emulations, and a practical perspective toward system-level applications is still lacking. In this review article, we discuss several important types of 2DM synapses for neuromorphic computing. Based on the cross-layer device-circuit-algorithm co-optimization strategy, non-ideal properties in 2DM synapses are considered for accelerating deep neural networks, and their impacts on system-level accuracy, power and area are discussed. Finally, a development guide of 2DM synapses is provided toward accurate online training and inference in the future.