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.
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Chen, Y., Hall, S., McDaid, L., Buiu, O., Kelly, P. (2006). A Silicon Synapse Based on a Charge Transfer Device for Spiking Neural Network Application. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_198
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DOI: https://doi.org/10.1007/11760191_198
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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