Lammie, Corey, Hamilton, Tara, and Azghadi, Mostafa Rahimi (2018) Live demonstration: Unsupervised character recognition with a FPGA neuromorphic system. In: Proceedings of the IEEE International Symposium on Circuits and Systems. From: ISCAS 2018: IEEE International Symposium on Circuits and Systems, 27-30 May 2018, Florence, Italy.

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Abstract

For this demonstration, we have implemented a Spiking Neural Network (SNN) on a Field Programmable Gate Array (FPGA) and trained it using Spike Timing Dependent Plasticity (STDP) to identify temporally encoded characters, in an unsupervised manner. The constructed one-layer network consists of plastic excitatory and non-plastic inhibitory synapses, which are connected to output Izhikevich neurons. The implemented neural hardware demonstrates a powerful and fast learning scheme, which brings about a significant unsupervised classification accuracy of 94 %.

Item ID:	54793
Item Type:	Conference Item (Scholarly Work)
Date Deposited:	30 Jul 2018 01:11
FoR Codes:	09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090601 Circuits and Systems @ 75% 10 TECHNOLOGY > 1006 Computer Hardware > 100699 Computer Hardware not elsewhere classified @ 25%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 50% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 50%
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