5 research outputs found
Neural-network dedicated processor for solving competitive assignment problems
A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques
Routing in Optical Multistage Interconnection Networks: a Neural Network Solution
There has been much interest in using optics to implement computer
interconnection networks. However, there has been little discussion of
any routing methodologies besides those already used in electronics.
In this paper, a neural network routing methodology is proposed that can
generate control bits for an optical multistage interconnection
network (OMIN). Though we present no optical implementation of this
methodology, we illustrate its control for an optical interconnection
network. These OMINs may be used as communication media for shared memory,
distributed computing systems.The routing methodology makes use of an
Artificial Neural Network (ANN) that functions as a parallel computer for
generating the routes. The neural network routing scheme may be applied to
electrical as well as optical interconnection networks.However, since
the ANN can be implemented using optics, this routing approach is especially
appealing for an optical computing environment. The parallel nature of the ANN
computation may make this routing scheme faster than conventional routing
approaches, especially for OMINs that are irregular. Furthermore, the neural
network routing scheme is fault-tolerant. Results are shown for generating
routes in a 16 times 16, 3 stage OMIN.
(Also cross-referenced as UMIACS-TR-94-21.