Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

Abstract 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 ionizati...

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Autores principales:	Sangya Dutta, Vinay Kumar, Aditya Shukla, Nihar R. Mohapatra, Udayan Ganguly
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe89b0e7b69647d093b0d805c0ff7b6a
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spelling	oai:doaj.org-article:fe89b0e7b69647d093b0d805c0ff7b6a2021-12-02T16:08:08ZLeaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET10.1038/s41598-017-07418-y2045-2322https://doaj.org/article/fe89b0e7b69647d093b0d805c0ff7b6a2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07418-yhttps://doaj.org/toc/2045-2322Abstract 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 (~1011 neuron based) large neural networks.Sangya DuttaVinay KumarAditya ShuklaNihar R. MohapatraUdayan GangulyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sangya Dutta Vinay Kumar Aditya Shukla Nihar R. Mohapatra Udayan Ganguly Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET
description	Abstract 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 (~1011 neuron based) large neural networks.
format	article
author	Sangya Dutta Vinay Kumar Aditya Shukla Nihar R. Mohapatra Udayan Ganguly
author_facet	Sangya Dutta Vinay Kumar Aditya Shukla Nihar R. Mohapatra Udayan Ganguly
author_sort	Sangya Dutta
title	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_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_sort	leaky integrate and fire neuron by charge-discharge dynamics in floating-body mosfet
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/fe89b0e7b69647d093b0d805c0ff7b6a
work_keys_str_mv	AT sangyadutta leakyintegrateandfireneuronbychargedischargedynamicsinfloatingbodymosfet AT vinaykumar leakyintegrateandfireneuronbychargedischargedynamicsinfloatingbodymosfet AT adityashukla leakyintegrateandfireneuronbychargedischargedynamicsinfloatingbodymosfet AT niharrmohapatra leakyintegrateandfireneuronbychargedischargedynamicsinfloatingbodymosfet AT udayanganguly leakyintegrateandfireneuronbychargedischargedynamicsinfloatingbodymosfet
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Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET

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