Synstor circuits for real-time adaptive intelligent systems

The foundation of current AI systems lies in computers that implement learning and inference procedures sequentially, resulting in learning latency, elevated power consumption, and diminished adapbility to unforeseen environmental changes. We overcome these issues by presenting a brain-inspired synaptic resistor (synstor) circuit, enabling concurrent real-time learning and inference. AI systems utilizing synstor circuits, such as those in drones and morphing wings, achieve significantly faster learning, better performance, greater energy efficiency, and improved adaptability compared to computer-based AI systems. Synstor circuits offer a promising path for AI systems to excel in dynamic and unpredictable real-world environments.

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Publication Details

Journal
Journal of Intelligent Material Systems and Structures
Published
2025-07-20
DOI
https://doi.org/10.1177/1045389x251349347
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Synstor circuits for real-time adaptive intelligent systems

Yong Chen
Journal of Intelligent Material Systems and Structures
Advanced Memory and Neural Computing
article

Synstor circuits for real-time adaptive intelligent systems

Yong Chen
article en

Abstract

The foundation of current AI systems lies in computers that implement learning and inference procedures sequentially, resulting in learning latency, elevated power consumption, and diminished adapbility to unforeseen environmental changes. We overcome these issues by presenting a brain-inspired synaptic resistor (synstor) circuit, enabling concurrent real-time learning and inference. AI systems utilizing synstor circuits, such as those in drones and morphing wings, achieve significantly faster learning, better performance, greater energy efficiency, and improved adaptability compared to computer-based AI systems. Synstor circuits offer a promising path for AI systems to excel in dynamic and unpredictable real-world environments.

Journal of Intelligent Material Systems and StructuresVol. 36(18-19)
University of California, Los Angeles (US)
Air Force Office of Scientific Research
Openalex Percentile: Top 84%
Advanced Memory and Neural Computing
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