An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model

Salma Hassan; Khaled N. Salama; Hassan Mostafa

doi:10.1109/MWSCAS.2018.8624028

An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model

Salma Hassan, Khaled N. Salama, Hassan Mostafa

King Abdullah University of Science and Technology

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

4 Scopus citations

Abstract

This paper proposes the use of approximate multipliers in the hardware implementation of Izhikevich spiking neuron model. The accuracy of the model is investigated by calculating various types of errors on a single neuron and this analysis shows that the proposed model follows the original model. It shows that the proposed model reproduces the same firing patterns as the original one. The network behavior is also studied and proved that the model has the same activity patterns of the original one. Moreover, the proposed neuron exhibits better accuracy than the piecewise linear approximation of the Izhikevich model.

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	492-495
Number of pages	4
ISBN (Print)	9781538673928
DOIs	https://doi.org/10.1109/MWSCAS.2018.8624028
State	Published - Feb 26 2019

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10.1109/MWSCAS.2018.8624028

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title = "An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model",

abstract = "This paper proposes the use of approximate multipliers in the hardware implementation of Izhikevich spiking neuron model. The accuracy of the model is investigated by calculating various types of errors on a single neuron and this analysis shows that the proposed model follows the original model. It shows that the proposed model reproduces the same firing patterns as the original one. The network behavior is also studied and proved that the model has the same activity patterns of the original one. Moreover, the proposed neuron exhibits better accuracy than the piecewise linear approximation of the Izhikevich model.",

author = "Salma Hassan 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.",

year = "2019",

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doi = "10.1109/MWSCAS.2018.8624028",

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booktitle = "2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)",

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An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model. / Hassan, Salma; Salama, Khaled N.; Mostafa, Hassan.
2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 492-495.

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

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T1 - An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model

AU - Hassan, Salma

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.

PY - 2019/2/26

Y1 - 2019/2/26

N2 - This paper proposes the use of approximate multipliers in the hardware implementation of Izhikevich spiking neuron model. The accuracy of the model is investigated by calculating various types of errors on a single neuron and this analysis shows that the proposed model follows the original model. It shows that the proposed model reproduces the same firing patterns as the original one. The network behavior is also studied and proved that the model has the same activity patterns of the original one. Moreover, the proposed neuron exhibits better accuracy than the piecewise linear approximation of the Izhikevich model.

AB - This paper proposes the use of approximate multipliers in the hardware implementation of Izhikevich spiking neuron model. The accuracy of the model is investigated by calculating various types of errors on a single neuron and this analysis shows that the proposed model follows the original model. It shows that the proposed model reproduces the same firing patterns as the original one. The network behavior is also studied and proved that the model has the same activity patterns of the original one. Moreover, the proposed neuron exhibits better accuracy than the piecewise linear approximation of the Izhikevich model.

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DO - 10.1109/MWSCAS.2018.8624028

M3 - Conference contribution

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

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

An Approximate Multiplier Based Hardware Implementation of the Izhikevich Model

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