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This paper presents a summary of four research projects presented at MICRONEURO 94, covering a variety of different hardware implementations of Artificial Neural Networks. The first two works describe optical and optoelectronic implementations. A combination of optics and electronics is described in the first work. An optical input plane for a neural net has been built so thatwhole images with tens of thousands of pixels can be entered into a network in parallel. In the second work, an all-optical network is presented, where not only the communication, but also the calculations, are done optically by using optically nonlinear materials. The third work addresses the issue of on-chip learning in analog implementations, by comparing the required precision for different learning schemes. It is observed that traditional algorithms such as back-propagation require a high resolution in the computation. The fourth work describes a digital VLSI circuit implementing a self-organizing feature map, an unsupervised learning technique. Also in this example one of the major problems is the resolution of the computation.
Artificial Neural Networks, VLSI Implementations, Optical Neural Networks, Learning, Precision Issues, Optoelectronics

L. M. Reyneri and H. P. Graf, "Neural Networks-Extraordinary Variation," in IEEE Micro, vol. 15, no. , pp. 48-59, 1995.
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