Field-free multistate spin–orbit torque devices for programmable image edge recognition circuit

The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications.

Authors

Institutions

Publication Details

Journal
Applied Physics Letters
Published
2024-09-02
DOI
https://doi.org/10.1063/5.0220711
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.83

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Field-free multistate spin–orbit torque devices for programmable image edge recognition circuit

Kaifeng Dong, Ying Tao, Rujun Tang, Liu Yang et al.
4 citations
Applied Physics Letters
Advanced Memory and Neural Computing
0.83
article

Field-free multistate spin–orbit torque devices for programmable image edge recognition circuit

Kaifeng Dong, Ying Tao, Rujun Tang, Liu Yang, Fang Jin, Wen‐Di Li, Huihui Li, Chao Zuo
article en
4 citations

Abstract

The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications.

Applied Physics LettersVol. 125(10)
Ministry of Education of the People's Republic of China (CN), China University of Geosciences (CN), Soochow University (CN), Shandong Institute of Automation (CN), Wuhan Ship Development & Design Institute (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.