1. Natural learning of neural networks by reconfiguration
- Author
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Spaanenburg, L., Alberts, R., Slump, Cornelis H., van der Zwaag, B.J., Rodriguez-Vazquez, Angel, Abbott, Derek, and Carmona, Ricardo
- Subjects
Modularity (networks) ,Artificial neural network ,business.industry ,Computer science ,Distributed computing ,Feed-Forward Neural Network ,Modularity ,Control reconfiguration ,Image processing ,Spatial Computing ,Modular design ,Wave Computing ,Field-Programmable Gate-Array ,METIS-211890 ,Cellular neural network ,Reconfiguration ,Feedforward neural network ,EWI-9668 ,Cellular Neural Network. Temporal computing ,IR-45308 ,Artificial intelligence ,Field-programmable gate array ,business - 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.
- Published
- 2003
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