ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models

Ahmed J. Abd El-Maksoud; Youssef O. Elmasry; Khaled N. Salama; Hassan Mostafa

doi:10.1109/MWSCAS.2018.8623858

ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models

Ahmed J. Abd El-Maksoud, Youssef O. Elmasry, Khaled N. Salama, Hassan Mostafa

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

2 Scopus citations

Abstract

This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate versions are carried out. Also, spiking behavior error between these models and the Hodgkin Huxley model, the reference accurate model, is presented. Finally, a fair comparative analysis is discussed to help the Spiking Neural Networks designers to select the best neuron model hardware implementation from the power, area and accuracy perspectives.

Original language	English (US)
Title of host publication	2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	504-507
Number of pages	4
ISBN (Print)	9781538673928
DOIs	https://doi.org/10.1109/MWSCAS.2018.8623858
State	Published - Feb 28 2019

Bibliographical note

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

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10.1109/MWSCAS.2018.8623858

Cite this

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title = "ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models",

abstract = "This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate versions are carried out. Also, spiking behavior error between these models and the Hodgkin Huxley model, the reference accurate model, is presented. Finally, a fair comparative analysis is discussed to help the Spiking Neural Networks designers to select the best neuron model hardware implementation from the power, area and accuracy perspectives.",

author = "El-Maksoud, {Ahmed J. Abd} and Elmasry, {Youssef O.} and Salama, {Khaled N.} and Hassan Mostafa",

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

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doi = "10.1109/MWSCAS.2018.8623858",

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ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models. / El-Maksoud, Ahmed J. Abd; Elmasry, Youssef O.; Salama, Khaled N. et al.
2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 504-507.

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

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AU - El-Maksoud, Ahmed J. Abd

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AU - Salama, Khaled N.

AU - Mostafa, Hassan

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

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Y1 - 2019/2/28

N2 - This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate versions are carried out. Also, spiking behavior error between these models and the Hodgkin Huxley model, the reference accurate model, is presented. Finally, a fair comparative analysis is discussed to help the Spiking Neural Networks designers to select the best neuron model hardware implementation from the power, area and accuracy perspectives.

AB - This paper introduces the hardware and the ASIC implementations of the four most popular biologically inspired neuron models. The models are quartic, Izhikevich, Hindmarsh Rose and Fitzhugh-Nagumo. Moreover, some approximate computing techniques are applied on these models to reduce the area and power consumption. In addition, ASIC implementations of these models and their approximate versions are carried out. Also, spiking behavior error between these models and the Hodgkin Huxley model, the reference accurate model, is presented. Finally, a fair comparative analysis is discussed to help the Spiking Neural Networks designers to select the best neuron model hardware implementation from the power, area and accuracy perspectives.

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U2 - 10.1109/MWSCAS.2018.8623858

DO - 10.1109/MWSCAS.2018.8623858

M3 - Conference contribution

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EP - 507

BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models

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