Integration of nanoscale memristor synapses in neuromorphic computing architectures
ETH Zurich · University of Zurich · +8 more institutions
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…
Citation impact
- FWCI
- 29.52
- Percentile
- 100%
- References
- 116
Authors
5- GIGiacomo IndiveriCorresponding
ETH Zurich, University of Zurich, Institute for Biomedical Engineering
- BLB. Linares-Barranco
Instituto de Microelectrónica de Sevilla
- RLRobert Legenstein
Graz University of Technology
- GDG. Deligeorgis
Centre National de la Recherche Scientifique, Laboratoire d'Analyse et d'Architecture des Systèmes, Université Fédérale de Toulouse Midi-Pyrénées
- TPThemis Prodromakis
University of Southampton, Imperial College London
Topics & keywords
- Neuromorphic engineering
- Memristor
- Computer science
- CMOS
- Massively parallel
- Computer architecture
- Artificial neural network
- Key (lock)
- Affordable and clean energy