An Attention Mechanism‐Based Adaptive Feedback Computing Component by Neuromorphic Ion Gated MoS2 Transistors. (16th December 2022)

• Record Type: Journal Article
• Authors: Liu, Chang; Wang, Yanghao; Zhang, Teng; Yuan, Rui; Yang, Yuchao
• Is Part Of: Advanced Electronic Materials. Volume 9:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-16
• Subjects: attention mechanism -- neuromorphic computing -- neuronal circuits -- synapse transistor; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202201060
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: Neuromorphic computing is expected to bridge cognitive behaviors with computing systems in an efficient, expandable, and biologically inspired way. A pivotal cognitive behavior is the attention mechanism, which is highly important in filtering and regulating spatio‐temporal information. Emerging neuromorphic devices hold prospect in utilizing their internal physical mechanisms for dynamic computing resources. Here, a basic top–down attention computing component consisting of a synaptic transistor and a neuron is proposed, where efficient information processing is realized by combining the inherent device dynamics and the feedback loop. A theoretical model is established in simulation to demonstrate the capabilities of such a computing system in information filtering and control. Notably, new dynamic circuit behaviors, such as conductance oscillation and activate function switching, are discovered from appropriate time parameters. The attention computing component contains rich dynamic behaviors, providing a power and area‐saving method to construct high‐complexity neuromorphic systems for spatio‐temporal signal preprocessing and control. Abstract : The work first establishes a feedback loop by connecting synaptic transistors to LIF neurons. Unit experiments and simulations can verify the adaptive properties of this feedback. In image processing and target tracking assignments, this attention computing component is shown to effectively filter and selectively process information from experimental data by using parametric models for large‐scale simulation of the devices.