NOVA: state assignment of finite state machines for optimal two-level logic implementations
DAC '89 Proceedings of the 26th ACM/IEEE Design Automation Conference
State assignment for hardwired VLSI control units
ACM Computing Surveys (CSUR)
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Sequential Logic Synthesis
Evolutionary Algorithm for State Assignment of Finite State Machines
DSD '02 Proceedings of the Euromicro Symposium on Digital Systems Design
FSM State Assignment Methods for Low-Power Design
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Integrated Power-Gating and State Assignment for Low Power FSM Synthesis
ISVLSI '08 Proceedings of the 2008 IEEE Computer Society Annual Symposium on VLSI
Genetic algorithm-based FSM synthesis with area-power trade-offs
Integration, the VLSI Journal
MUSE: a multilevel symbolic encoding algorithm for state assignment
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Optimal State Assignment for Finite State Machines
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
MUSTANG: state assignment of finite state machines targeting multilevel logic implementations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova.