A VLSI neuroprocessor for dynamic assignment of resources

An analog processor for dynamic assignment of resources is proposed. The neural-network-inspired processor consists of a matrix of processing elements where columns are associated with resources and rows with consumers. Analog cost-of-association values are programmed into each element. Each row and column is overseen by a winner-take-all network that serves to enforce input and output blocking constraints by dynamically controlling the number of active elements in that row or column. After the processor has settled on a solution, the pattern of active and inactive elements gives the required assignment configuration. The architectural issues, the innovations and optimization introduced, and the optimization processor are discussed. Simulation results show that for a 64-resource problem, on the average, an optimal or near optimal solution can be found in a few hundred microseconds. This is orders of magnitude faster than solutions obtained by sequential computing technology