State assignment for area minimization of sequential circuits based on cuckoo search optimization. (May 2015)

• Record Type: Journal Article
• Authors: El-Maleh, Aiman H.; Sait, Sadiq M.; Bala, Abubakar
• Is Part Of: Computers & electrical engineering. Volume 44(2015)
• Page Start: 13
• Page End: 23
• Publication Date: 2015-05
• Subjects: Cuckoo search -- State Assignment -- Heuristics -- Sequential circuit -- Area minimization -- Finite state machines; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.03.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A major optimization problem in synthesis of sequential circuits is State Assignment (SA). Cuckoo search optimization (CSO) algorithm is employed for solving the SA problem. CSO targets area minimization of synthesized sequential circuits. CSO results outperform deterministic and non-deterministic heuristic optimization methods. Abstract: A major optimization problem in the synthesis of sequential circuits is State Assignment or State Encoding in Finite State Machines (FSMs). The state assignment of an FSM determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation. Since optimal state assignment is an NP-hard problem and existing deterministic algorithms produce solutions far from best known solutions, we resort to the use of non-deterministic iterative optimization heuristics. This paper proposes the use of cuckoo search optimization (CSO) algorithm for solving the state assignment problem (SAP) of FSMs with the aim of minimizing area of the resulting sequential circuit. Results obtained from the CSO algorithm are compared with those obtained from binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), and the well-known deterministic methods of NOVA and JEDI. The results indicate that CSO outperforms deterministic methods as well as other non-deterministic heuristic optimization methods.