Integration of nanoscale memristor synapses in neuromorphic computing architectures (bibtex)
by , , , ,
Abstract:
Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of informatioIE
Reference:
Integration of nanoscale memristor synapses in neuromorphic computing architectures (G. Indiveri, B. Linares-Barranco, R. Legenstein, G. Deligeorgis, T. Prodromakis), In Nanotechnology (T.M. Bernard, ed.), volume 24, 2013.
Bibtex Entry:
@Article{Indiveri_etal13,
author		= {G. Indiveri and B. Linares-Barranco and R. Legenstein and
		  G. Deligeorgis and T. Prodromakis},
title		= {Integration of nanoscale memristor synapses in
		  neuromorphic computing architectures},
journal		= {Nanotechnology},
year		= {2013},
volume		= {24},
number		= {38},
pages		= {384010},
doi		= {10.1088/0957-4484/24/38/384010},
url		= {http://stacks.iop.org/0957-4484/24/i=38/a=384010},
abstract	= {Conventional neuro-computing architectures and artificial
		  neural networks have often been developed with no or loose
		  connections to neuroscience. As a consequence, they have
		  largely ignored key features of biological neural
		  processing systems, such as their extremely low-power
		  consumption features or their ability to carry out robust
		  and efficient computation using massively parallel arrays
		  of limited precision, highly variable, and unreliable
		  components. Recent developments in nano-technologies are
		  making available extremely compact and low power, but also
		  variable and unreliable solid-state devices that can
		  potentially extend the offerings of availing CMOS
		  technologies. In particular, memristors are regarded as a
		  promising solution for modeling key features of biological
		  synapses due to their nanoscale dimensions, their capacity
		  to store multiple bits of informatioIE},
editor		= {T.M. Bernard},
address		= {Bellingham, WA},
series		= {Proc. SPIE},
url		= {http://ncs.ethz.ch/pubs/pdf/Kramer_Indiveri98.pdf}
}
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