Describes a special purpose, very high speed, digital deterministic Boltzmann neural network VLSI chip. Each chip has 32 physical neural processors, which can be apportioned into an arbitrary topology (input, multiple hidden and output layers) of up to 160 virtual neurons total. Under typical conditions, the chip learns at approximately 5*10/sup 8/ connection updates/second (CUPS). Through relatively minor (subsequent) modifications, the authors' chips can be 'tiled' in multi-chip modules, to make multi-layer networks of arbitrary size suffering only slight communications delays and overhead. In this way, the number of CUPS can be made arbitrarily large, limited only by the number of chips tiled. The chip's high speed is due to massively parallel array computation of the inner products of connection weights and neural activations, limited (but adequate) precision for weights and activations (5 bits), high clock rate (180 MHz), as well as several algorithmic and design insights.>

A system for the extraction of minutiae from gray-scale fingerprint images using back-propagation networks and Gabor filters is described. Good detection ratios and low false alarm rates were achieved. The importance of having good input representations of the image data is illustrated through variations in the performance of different trained networks. The usefulness of Gabor filters in the representation processing layer is demonstrated