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Single SiGe Transistor Based Energy-Efficient Leaky Integrate-and-Fire Neuron for Neuromorphic Computing | Neural Processing Letters Skip to main content

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Single SiGe Transistor Based Energy-Efficient Leaky Integrate-and-Fire Neuron for Neuromorphic Computing

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Abstract

This work aims to present a novel energy-efficient single transistor leaky integrate-and-fire neuron for future neuromorphic computing. It comprises of a SiGe-based MOSFET, having channel length of 400 nm. Using 2D simulation, it has been verified that the proposed SiGe-based single transistor neuron accurately mimics the spiking behavior of the biological neuron, while eliminating the need of external circuitry and exorbitant energy consumption. The neuron consumes energy of 3.8 pJ/spike, which is 11.8 times and 2.1 times lesser than the previously proposed Si-based and Ge-based single transistor neurons, respectively. It also shows improvement in terms of controllability, simplicity, integration density, and fabrication process. By designing threshold logic gates, the proposed neuron has been employed to implement universal digital logic functions, such as NAND and NOR. Finally, the recognition ability for MNIST handwritten digits has been verified. It has been confirmed that besides imitating the neuronal behavior accurately, the proposed neuron can also be used in practical spiking neural networks for image classification with an accuracy of 93.79%.

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Authors and Affiliations

  1. Department of Electronics & Instrumentation Technology, University of Kashmir, Srinagar, J&K, India

    Mudasir A. Khanday, Farooq A. Khanday & Faisal Bashir

Authors
  1. Mudasir A. Khanday
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  2. Farooq A. Khanday
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  3. Faisal Bashir
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Contributions

MAK: Conceptualization, Methodology, Software, Investigation, Writing- Original draft preparation. FAK: Supervision, Conceptualization, Writing- Original draft preparation, Reviewing and Editing. FB: Software, Writing-Original draft preparation, Reviewing and Editing.

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Correspondence to Farooq A. Khanday.

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Khanday, M.A., Khanday, F.A. & Bashir, F. Single SiGe Transistor Based Energy-Efficient Leaky Integrate-and-Fire Neuron for Neuromorphic Computing. Neural Process Lett 55, 6997–7007 (2023). https://doi.org/10.1007/s11063-023-11245-w

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