TY - GEN
T1 - ASIC Oriented Comparative Analysis Of Biologically Inspired Neuron Models
AU - El-Maksoud, Ahmed J. Abd
AU - Elmasry, Youssef O.
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 - 2019/2/28
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.
UR - http://hdl.handle.net/10754/652977
UR - https://ieeexplore.ieee.org/document/8623858
UR - http://www.scopus.com/inward/record.url?scp=85062212606&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2018.8623858
DO - 10.1109/MWSCAS.2018.8623858
M3 - Conference contribution
SN - 9781538673928
SP - 504
EP - 507
BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -