Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/mwscas.2018.8623858 | Midwest Symposium on Circuits and Systems Conference Proceedings |
Keywords | Field | DocType |
Neuromorphic computing,spiking neural networks,biologically inspired models | Biological neuron model,Computer science,Neuromorphic engineering,Implementation,Application-specific integrated circuit,Control engineering,Quartic function,Spiking neural network,Computer engineering,Hodgkin–Huxley model,Power consumption | Conference |
ISSN | Citations | PageRank |
1548-3746 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed J. Abd El-Maksoud | 1 | 0 | 0.34 |
| Youssef O. Elmasry | 2 | 0 | 0.34 |
| Khaled N. Salama | 3 | 345 | 46.11 |
| Hassan Mostafa | 4 | 116 | 51.49 |