TY - GEN
T1 - Unsupervised character recognition with a simplified FPGA neuromorphic system
AU - Lammie, Corey
AU - Hamilton, Tara
AU - Azghadi, Mostafa Rahimi
PY - 2018
Y1 - 2018
N2 - ![CDATA[Neuromorphic hardware platforms have demonstrated significant promise in cognitive tasks such as visual processing and classification. These platforms usually consist of several layers of spiking neurons for feature extraction and various learning mechanisms, which renders the associated networks power and hardware hungry. In this paper, we have implemented a simplified proof-of-concept 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 excitatory synapses, which receive input characters in the form of Poissonian spike trains from the pre-synaptic side. From the post-synaptic side, the synapses are connected to output Izhikevich neurons. In addition, non-plastic inhibitory synapses between the output neurons are introduced to implement lateral inhibition and competitive learning. The implemented neural hardware demonstrates a powerful and fast learning scheme, which brings about a significant unsupervised classification accuracy of 94 %. In addition, since the proposed network receives the characters in the form of spike trains, it is amenable to being interfaced to neuromorphic event-driven sensors such as silicon retina, making the proposed platform useful for online unsupervised template matching applications.]]
AB - ![CDATA[Neuromorphic hardware platforms have demonstrated significant promise in cognitive tasks such as visual processing and classification. These platforms usually consist of several layers of spiking neurons for feature extraction and various learning mechanisms, which renders the associated networks power and hardware hungry. In this paper, we have implemented a simplified proof-of-concept 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 excitatory synapses, which receive input characters in the form of Poissonian spike trains from the pre-synaptic side. From the post-synaptic side, the synapses are connected to output Izhikevich neurons. In addition, non-plastic inhibitory synapses between the output neurons are introduced to implement lateral inhibition and competitive learning. The implemented neural hardware demonstrates a powerful and fast learning scheme, which brings about a significant unsupervised classification accuracy of 94 %. In addition, since the proposed network receives the characters in the form of spike trains, it is amenable to being interfaced to neuromorphic event-driven sensors such as silicon retina, making the proposed platform useful for online unsupervised template matching applications.]]
KW - field programmable gate arrays
KW - neural networks (computer science)
KW - neuromorphics
KW - optical character recognition
UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:46699
U2 - 10.1109/ISCAS.2018.8351532
DO - 10.1109/ISCAS.2018.8351532
M3 - Conference Paper
SN - 9781538648810
BT - 2018 IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings, 27-30 May 2018, Florence, Italy
PB - IEEE
T2 - IEEE International Symposium on Circuits and Systems
Y2 - 27 May 2018
ER -