18 April 2003 Natural learning of neural networks by reconfiguration
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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.
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Lambert Spaanenburg, R. Alberts, Cornelis H. Slump, B. J. vanderZwaag, "Natural learning of neural networks by reconfiguration", Proc. SPIE 5119, Bioengineered and Bioinspired Systems, (18 April 2003); doi: 10.1117/12.499549; https://doi.org/10.1117/12.499549
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