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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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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.

Keywords:

artificial neural network artificial synapse intelligent systems neuromorphic devices neuromorphic perception synaptic device topological insulator

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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.