HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system

Computers based on the Turing model execute artificial intelligence (AI) algorithms that are either programmed by humans or derived from machine learning. These AI algorithms cannot be modified during the operation process according to environmental changes, resulting in significantly poorer adaptability to new environments, longer learning latency, and higher power consumption compared to the human brain. In contrast, neurobiological circuits can function while simultaneously adapting to changing conditions. Here, we present a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning. Without any prior learning, a circuit of synaptic resistors integrating ferroelectric HfZrO materials was demonstrated to navigate a drone toward a target position while avoiding obstacles in a simulated environment, exhibiting significantly superior learning speed, performance, power consumption, and adaptability compared to computer-based artificial neural networks. Synaptic resistor circuits enable efficient and adaptive Super-Turing AI systems in uncertain and dynamic real-world environments.

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Publication Details

Journal
Science Advances
Published
2025-02-28
DOI
https://doi.org/10.1126/sciadv.adr2082
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
4.38
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article

HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system

Zixuan Rong, Dawei Gao, Atharva Deo, Rahul Shenoy et al.
6 citations
Science Advances
Advanced Memory and Neural Computing
4.38
article

HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system

Zixuan Rong, Dawei Gao, Atharva Deo, Rahul Shenoy, J. Joshua Yang, Su‐in Yi, Shiva Asapu, Dhruva Nathan, Richard Stanley Williams, Jung-Min Lee, Yong Chen, Qing Wu, Jian‐Guo Zheng, Mingjie Xu, Dharma Paladugu, Yong Qiang Hei, David Qiao
article en
6 citations

Abstract

Computers based on the Turing model execute artificial intelligence (AI) algorithms that are either programmed by humans or derived from machine learning. These AI algorithms cannot be modified during the operation process according to environmental changes, resulting in significantly poorer adaptability to new environments, longer learning latency, and higher power consumption compared to the human brain. In contrast, neurobiological circuits can function while simultaneously adapting to changing conditions. Here, we present a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning. Without any prior learning, a circuit of synaptic resistors integrating ferroelectric HfZrO materials was demonstrated to navigate a drone toward a target position while avoiding obstacles in a simulated environment, exhibiting significantly superior learning speed, performance, power consumption, and adaptability compared to computer-based artificial neural networks. Synaptic resistor circuits enable efficient and adaptive Super-Turing AI systems in uncertain and dynamic real-world environments.

Science AdvancesVol. 11(9)
University of Southern California (US), United States Air Force Research Laboratory (US), California NanoSystems Institute (US), University of California, Irvine (US), University of Massachusetts Amherst (US), U.S. Air Force Research Laboratory Information Directorate, Texas A&M University (US)
Openalex Percentile: Top 6%
Advanced Memory and Neural Computing
4.38
DOIOpen Access PDFOpenAlex
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