A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing

Aya A. Ismail; Zeinab A. Shaheen; Osama Rashad; Khaled N. Salama; Hassan Mostafa

doi:10.1109/icm.2018.8704080

A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing

Aya A. Ismail, Zeinab A. Shaheen, Osama Rashad, Khaled N. Salama, Hassan Mostafa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

This paper introduces the hardware implementation of one of the most popular spiking neuron models which is Izhikevich model. The main target of this implementation is to reduce area and power consumed by the Spiking Neural Network (SNN) neurons as the SNN consists of a large number of neurons to mimic the human brain. Therefore, stochastic computing techniques are used to perform the squaring term that consumes much of the power in the Izhikevich neuron model equations. A hardware implementation of the model is proposed to show the area and power consumption to help the SNN designers to choose between stochastic-based multipliers and the approximate multipliers considering their power, area, and accuracy constraints.

Original language	English (US)
Title of host publication	2018 30th International Conference on Microelectronics (ICM)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	315-318
Number of pages	4
ISBN (Print)	9781538681671
DOIs	https://doi.org/10.1109/icm.2018.8704080
State	Published - May 2 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology, Egypt and Cairo University, Egypt.

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10.1109/icm.2018.8704080

Cite this

@inproceedings{2c98f863ac594d6488c9fdc0100cdb9b,

title = "A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing",

abstract = "This paper introduces the hardware implementation of one of the most popular spiking neuron models which is Izhikevich model. The main target of this implementation is to reduce area and power consumed by the Spiking Neural Network (SNN) neurons as the SNN consists of a large number of neurons to mimic the human brain. Therefore, stochastic computing techniques are used to perform the squaring term that consumes much of the power in the Izhikevich neuron model equations. A hardware implementation of the model is proposed to show the area and power consumption to help the SNN designers to choose between stochastic-based multipliers and the approximate multipliers considering their power, area, and accuracy constraints.",

author = "Ismail, {Aya A.} and Shaheen, {Zeinab A.} and Osama Rashad and Salama, {Khaled N.} and Hassan Mostafa",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology, Egypt and Cairo University, Egypt.",

year = "2019",

month = may,

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doi = "10.1109/icm.2018.8704080",

language = "English (US)",

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pages = "315--318",

booktitle = "2018 30th International Conference on Microelectronics (ICM)",

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A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing. / Ismail, Aya A.; Shaheen, Zeinab A.; Rashad, Osama et al.
2018 30th International Conference on Microelectronics (ICM). Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 315-318.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing

AU - Ismail, Aya A.

AU - Shaheen, Zeinab A.

AU - Rashad, Osama

AU - Salama, Khaled N.

AU - Mostafa, Hassan

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was partially funded by ONE Lab at Zewail City of Science and Technology, Egypt and Cairo University, Egypt.

PY - 2019/5/2

Y1 - 2019/5/2

N2 - This paper introduces the hardware implementation of one of the most popular spiking neuron models which is Izhikevich model. The main target of this implementation is to reduce area and power consumed by the Spiking Neural Network (SNN) neurons as the SNN consists of a large number of neurons to mimic the human brain. Therefore, stochastic computing techniques are used to perform the squaring term that consumes much of the power in the Izhikevich neuron model equations. A hardware implementation of the model is proposed to show the area and power consumption to help the SNN designers to choose between stochastic-based multipliers and the approximate multipliers considering their power, area, and accuracy constraints.

AB - This paper introduces the hardware implementation of one of the most popular spiking neuron models which is Izhikevich model. The main target of this implementation is to reduce area and power consumed by the Spiking Neural Network (SNN) neurons as the SNN consists of a large number of neurons to mimic the human brain. Therefore, stochastic computing techniques are used to perform the squaring term that consumes much of the power in the Izhikevich neuron model equations. A hardware implementation of the model is proposed to show the area and power consumption to help the SNN designers to choose between stochastic-based multipliers and the approximate multipliers considering their power, area, and accuracy constraints.

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U2 - 10.1109/icm.2018.8704080

DO - 10.1109/icm.2018.8704080

M3 - Conference contribution

SN - 9781538681671

SP - 315

EP - 318

BT - 2018 30th International Conference on Microelectronics (ICM)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

A Low Power Hardware Implementation of Izhikevich Neuron using Stochastic Computing

Abstract

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