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Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET
Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557947/ https://www.ncbi.nlm.nih.gov/pubmed/28811481 http://dx.doi.org/10.1038/s41598-017-07418-y |
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author | Dutta, Sangya Kumar, Vinay Shukla, Aditya Mohapatra, Nihar R. Ganguly, Udayan |
author_facet | Dutta, Sangya Kumar, Vinay Shukla, Aditya Mohapatra, Nihar R. Ganguly, Udayan |
author_sort | Dutta, Sangya |
collection | PubMed |
description | Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10(11) neuron based) large neural networks. |
format | Online Article Text |
id | pubmed-5557947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55579472017-08-16 Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET Dutta, Sangya Kumar, Vinay Shukla, Aditya Mohapatra, Nihar R. Ganguly, Udayan Sci Rep Article Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10(11) neuron based) large neural networks. Nature Publishing Group UK 2017-08-15 /pmc/articles/PMC5557947/ /pubmed/28811481 http://dx.doi.org/10.1038/s41598-017-07418-y Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Dutta, Sangya Kumar, Vinay Shukla, Aditya Mohapatra, Nihar R. Ganguly, Udayan Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title | Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title_full | Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title_fullStr | Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title_full_unstemmed | Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title_short | Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET |
title_sort | leaky integrate and fire neuron by charge-discharge dynamics in floating-body mosfet |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557947/ https://www.ncbi.nlm.nih.gov/pubmed/28811481 http://dx.doi.org/10.1038/s41598-017-07418-y |
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