A silicon synapse based on a charge transfer device for spiking neural network application

Authors:
Yajie Chen;Steve Hall;Liam McDaid;Octavian Buiu;Peter Kelly
Affiliations:
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK;Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK;School of Computing and Intelligent Systems, University of Ulster, Londonderry, Northern Ireland, UK;Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK;School of Computing and Intelligent Systems, University of Ulster, Londonderry, Northern Ireland, UK
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

We propose a silicon synapse for spiking neural network application. In this endeavor, two major issues are addressed: the structure of the synapse and the associated behavior. This synaptic structure is basically a charge transfer device comprising of two Metal-Oxide-Semiconductor (MOS) capacitors the first of which stores the weight and the second controls its reading. In this work, simulation results prove that the proposed synapse captures the intrinsic dynamics of the biological synapse and exhibits a spike characteristic. The device operates at very low power and offers the potential for scaling to massively parallel third generation hardware neural networks.