2024-03-29T10:02:50Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/844442020-07-03T10:43:24Zcom_10261_90com_10261_4col_10261_973
Perez-Carrasco, J. A.
Zamarreño-Ramos, Carlos
Serrano-Gotarredona, Teresa
Linares-Barranco, Bernabé
2013-10-17T11:25:09Z
2013-10-17T11:25:09Z
2010
Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS): 1659-1662 (2010)
http://hdl.handle.net/10261/84444
10.1109/ISCAS.2010.5537484
Neuromorphic circuits and systems techniques have great potential for exploiting novel nanotechnology devices, which suffer from great parametric spread and high defect rate. In this paper we explore some potential ways of building neural network systems for sophisticated pattern recognition tasks using memristors. We will focus on spiking signal coding because of its energy and information coding efficiency, and concentrate on Convolutional Neural Networks because of their good scaling behavior, both in terms of number of synapses and temporal processing delay. We propose asynchronous architectures that exploit memristive synapses with specially designed neurons that allow for arbitrary scalability as well as STDP learning. We present some behavioral simulation results for small neural arrays using electrical circuit simulators, and system level spike processing results on human detection using a custom made event based simulator.
eng
closedAccess
On neuromorphic spiking architectures for asynchronous STDP memristive systems
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