FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm

Abstract

In this paper we describe the engineering of a non-deterministic iterative heuristic [1] known as simulated evolution(SimE) to solve the well-known NP-hard state assignment problem (SAP). Each assignment of a code to a state isgiven a Goodness value derived from a matrix representation of the desired adjacency graph (DAG) proposed byAmaral et.al [2]. We use the (DAGa) proposed in previous studies to optimize the area, and propose a new DAGpand employ it to reduce the power dissipation. In the process of evolution, those states that have high Goodness havea smaller probability of getting perturbed, while those with lower Goodness can be easily reallocated. States areassigned to cells of a Karnaugh-map, in a way that those states that have to be close in terms of Hamming distanceare assigned adjacent cells. Ordered weighed average (OWA) operator proposed by Yager [3] is used to combine thetwo objectives. Results are compared with those published in previous studies, for circuits obtained from the MCNCbenchmark suite. It was found that the SimE heuristic produces better quality results in most cases, and/or in lessertime, when compared to both deterministic heuristics and non-deterministic iterative heuristics such as GeneticAlgorithm.

Adaptation of the simulated evolution algorithm for wind farm layout optimization

“…Inspired by the theory of evolution, the algorithm has several distinctive features as mentioned in Section 1. Despite its distinctive features and excellent performance, the algorithm and its hybrid variants have received little attention from researchers in some domains such as healthcare [31], internet traffic engineering [18], network design optimization [6,32], microelectronics [33][34][35], and cloud computing [17,36,37].…”

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

“…Through using this method, a set of non-dominated solutions can be obtained at the cost of a long evolutionary time. Sait et al used simulated evolution algorithm (SimE) [18] to solve the problem by combining the area and power objectives, and SimE heuristic produced better quality results in most cases, and/or in lesser time, when compared to both deterministic heuristics and non-deterministic iterative heuristics.…”

“…Khan comprehensively studied state assignment methods for power minimisation of FSMs. GA and Tabu‐search are utilised as search space exploration tools in searching for lower area and power solutions [18]. It concluded that cost saving can be achieved by using a more accurate cost measure, but come with more computational time.…”

“…Researchers have tried to use EA‐based state assignment to achieve low area and power for FSM [18, 21, 25]. Olson et al first took into account area into their cost equation for low‐power FSM realisation [26].…”

“…Evolutionary algorithm (EA) [16] has been successfully applied in achieving low-area and low-power FSMs [17][18][19][20]. However, some researchers suspected the EAs in solving state assignment problem (SAP), which takes the entire subsequent step for low power including the logic synthesis, technology mapping and simulation, is practice and applicable.…”