Microcode optimization with neural networks

Abstract

Abstract-Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatiblity classes), and then use the proposed second method to optimize on the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set.

Algoritmos para compressão de microcodigo

“…Jayasri e Basu [70] modelaram o problema utilizando programação linear inteira e Baer e Koyama [14] propuseram um algoritmo Branch-and-Bound para agrupar as micro-operações. Em 1979, Robertson [113] provou que o problema é NP-Completo e, desde então, diversas heurísticas foram propostas para solucionar o problema [19,63,75,98,106,109,111,112,121].…”