A Probabilistic Tabu Search State Assignment Algorithm for Area and Power Optimization of Sequential Circuits

Abstract

State assignment is the most critical step in the synthesis and optimization of sequential circuits as it significantly impacts sequential circuits area and power. Due to the high complexity of the state assignment problem and the ineffectiveness of existing deterministic algorithms in generating good solutions, evolutionary algorithms can be employed to obtain more effective state assignment solutions. In this paper, we propose a probabilistic tabu search (PTS) state assignment algorithm that is employed for the optimization of area and power of sequential circuits. The proposed algorithm is based on tabu search with the addition of exploiting code swap probabilities to prune the search space more effectively. Code swap probabilities are updated dynamically during the execution of the algorithm in such a way that code swaps that result in relatively inferior solutions will be assigned lower probabilities while those that result in good solutions will be assigned higher probabilities. In addition, tabu list size is changed dynamically during execution to help the algorithm get out of local minima. Based on experimental results, we show that the proposed PTS state assignment algorithm is more effective than all existing state assignment algorithms as it generates state assignment solutions with significantly lower area and power.