Live demonstration: Unsupervised character recognition with a FPGA neuromorphic system

Conference Contribution ResearchOnline@JCU
Lammie, Corey;Hamilton, Tara;Azghadi, Mostafa Rahimi
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 %.

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ISCAS 2018: IEEE International Symposium on Circuits and Systems

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1

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Florence, Italy

Publisher

Institute of Electrical and Electronics Engineers

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Piscataway, NJ, USA

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DOI

10.1109/ISCAS.2018.8351790