@inproceedings{91016d5a933d427494b41bef1e7b9303,
title = "Natural learning of neural networks by reconfiguration",
abstract = "The communicational and computational demands of neural networks are hard to satisfy in a digital technology. Temporal computing addresses this problem by iteration, but leaves a slow network. Spatial computing only became an option with the coming of modern FPGA devices. The paper provides two examples. First the balance between area and time is discussed on the realization of a modular feed-forward network. Second, the design of real-time image processing through a Cellular Neural Network is treated. In both examples, reconfiguration can be applied to provide for a natural and transparent support of learning.",
keywords = "METIS-211890, Spatial Computing, Wave Computing, Field-Programmable Gate-Array, Feed-Forward Neural Network, EWI-9668, Cellular Neural Network. Temporal computing, Reconfiguration, Modularity, IR-45308",
author = "L. Spaanenburg and R. Alberts and Slump, {Cornelis H.} and {van der Zwaag}, B.J.",
note = "SAS 11-03 ; null ; Conference date: 19-05-2003 Through 21-05-2003",
year = "2003",
month = may,
doi = "10.1117/12.499549",
language = "Undefined",
isbn = "0-8194-4979-2",
series = "Proceedings of SPIE",
publisher = "SPIE",
pages = "273--284",
editor = "Angel Rodriguez-Vazquez and Derek Abbott and Ricardo Carmona",
booktitle = "Bioengineered and Bioinspired Systems",
address = "United States",
}