State assignment for sequential circuits using multi-objective genetic algorithm

Majority‐based evolution state assignment algorithm for area and power optimisation of sequential circuits

“…MBE achieved better results than NOVA [4] for all the compared circuits. In addition, it achieved better results than GA [17] and SA [19] in eight out of the nine compared circuits.…”

“…We have run the MBE algorithm targeting the optimisation of the number of product terms and compared the results with two recent implementations based on GA [17] and SA [19]. Table 6 shows the number of product terms obtained by the proposed MBE algorithm, NOVA [4], GA [17] and SA [19]. MBE achieved better results than NOVA [4] for all the compared circuits.…”

“…Owing to the complexity of the SA problem and the limitations of existing deterministic algorithms, non‐deterministic evolutionary algorithms have been employed. These include genetic algorithm (GA) [10–17], simulated annealing (SA) [6, 18, 19], Tabu search (TS) [20], simulation evolution (SimE) [21], binary particle swarm optimisation (BPSO) [22] and other evolutionary algorithms [23, 24].…”

MPGA: an evolutionary state assignment for dynamic and leakage power reduction in FSM synthesis

“…Quantum‐inspired EA is also employed as a solver for state assignment in synchronous FSMs [25]. In addition, multi‐objective evolutionary algorithm was employed to search low‐power state assignment by simultaneously optimising area and SA [5, 30]. Jassani used MOGA to find a state assignment that makes the product of Hamming distance between states and state transition probability minimised, so that the area and SA can be both reduced.…”

“…Table 1 presents SA and leakage power of two state assignments. The leakage power estimation of combinational part for two state assignments is calculated by using the model from [30]. SA is calculated by (4).…”