A Multiple-State Ion Synaptic Transistor Applicable to Abnormal Car Detection with Transfer Learning

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An artificial synapse is an essential element to construct a hardware-based artificial neural network (ANN). While various synaptic devices have been proposed along with studies on electrical characteristics and proper applications, a small number of conductance states with nonlinear and asymmetric conductance changes have been problematic and imposed limits on computational performance. Their applications are thus still limited to the classification of simple images or acoustic datasets. Herein, a polymer electrolyte-gated synaptic transistor (pEGST) is demonstrated for video-based learning and inference using transfer learning. In particular, abnormal car detection (ACD) is attempted with video-based learning and inference to avoid traffic accidents. The pEGST showed multiple states of 8,192 (=13 bits) for weight modulation with linear and symmetric conductance changes and helped reduce the error rate to 3% to judge whether a car in a video is abnormal.
Publisher
WILEY
Issue Date
2022-06
Language
English
Article Type
Article
Citation

ADVANCED INTELLIGENT SYSTEMS, v.4, no.6

DOI
10.1002/aisy.202100231
URI
http://hdl.handle.net/10203/297136
Appears in Collection
CBE-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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