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Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems
Dani S Assi1, Hongli Huang1, Vaithinathan Karthikeyan1
1Electronics and Nanoscale Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 21, 2023
Summary
Quantum topological neuristors (QTNs) offer ultra-low energy consumption and high switching speeds for advanced neuromorphic computing. These bioinspired devices mimic brain synapses, paving the way for next-generation intelligent machines.
Area of Science:
- Materials Science
- Computer Science
- Artificial Intelligence
Background:
- Neuromorphic artificial intelligence (AI) systems are crucial for high-performance computing but face slow advancement without specialized device designs.
- Mimicking mammalian brain synapses is key to developing efficient neuromorphic systems.
Purpose of the Study:
- To introduce a new class of quantum topological neuristors (QTNs) for ultra-low energy consumption and high-speed neuromorphic computing.
- To demonstrate biomimicking synaptic behavior using QTNs.
Main Methods:
- Designed quantum topological neuristors (QTNs) utilizing quantum topological insulator (QTI) materials.
- Investigated edge state transport and tunable energy gaps within QTI materials to achieve bioinspired neural network characteristics.
- Trained QTNs using a hand gesture game interfaced with artificial neural networks for decision-making tasks.
Main Results:
- QTNs exhibit ultralow energy consumption (pJ) and high switching speeds (µs).
- Demonstrated effective learning, relearning, and forgetting stages in neuromorphic behavior through augmented device and QTI material design.
- Successfully emulated real-time neuromorphic efficiency by training QTNs for decision-making operations.
Conclusions:
- QTNs possess significant potential for next-generation neuromorphic computing.
- The developed QTNs can contribute to the advancement of intelligent machines and humanoids.
Keywords:
artificial neural networkartificial synapseintelligent systemsneuromorphic devicesneuromorphic perceptionsynaptic devicetopological insulatorMore Related Videos
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