In the field of the Artificial Neural Networks, multichip architecture can be effectively used to implement very large networks. The availability of large neural electronic systems can represent a really useful tool to deeply and effectively investigate on innovative, “bio-inspired”, computational paradigms. In this paper, the authors present a technique to reduce the I/O analogue pins of about 87%, previously applied from the authors to Cellular Neural Networks, well suited for neuromorphic neural networks.In the field of the Artificial Neural Networks, multichip architecture can be effectively used to implement very large networks. The availability of large neural electronic systems can represent a really useful tool to deeply and effectively investigate on innovative, “bio-inspired”, computational paradigms. In this paper, the authors present a technique to reduce the I/O analogue pins of about 87%, previously applied from the authors to Cellular Neural Networks, well suited for neuromorphic neural networks.
Bonaiuto, V., Sargeni, F. (2010). Multi-chip integrate and fire neural network architecture. In Proceedings of the 15th IEEE mediterranean electrotechnical conference (MELECON 2010) (pp.630-634) [10.1109/MELCON.2010.5476009].
Multi-chip integrate and fire neural network architecture
BONAIUTO, VINCENZO;SARGENI, FAUSTO
2010-01-01
Abstract
In the field of the Artificial Neural Networks, multichip architecture can be effectively used to implement very large networks. The availability of large neural electronic systems can represent a really useful tool to deeply and effectively investigate on innovative, “bio-inspired”, computational paradigms. In this paper, the authors present a technique to reduce the I/O analogue pins of about 87%, previously applied from the authors to Cellular Neural Networks, well suited for neuromorphic neural networks.In the field of the Artificial Neural Networks, multichip architecture can be effectively used to implement very large networks. The availability of large neural electronic systems can represent a really useful tool to deeply and effectively investigate on innovative, “bio-inspired”, computational paradigms. In this paper, the authors present a technique to reduce the I/O analogue pins of about 87%, previously applied from the authors to Cellular Neural Networks, well suited for neuromorphic neural networks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
