dor_id: 45586

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/344/341

100.1.#.a: M. Sait, Sadiq; C. Oughali, F.; M. Arafeh, A.

524.#.#.a: M. Sait, Sadiq, et al. (2012). FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm. Journal of Applied Research and Technology; Vol. 10 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45586

245.1.0.a: FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2012

264.#.1.c: 2012-12-01

653.#.#.a: EDA; FSM Synthesis; State Encoding; Simulated Evolution; Multiobjective Optimization; Non-Deterministic Algorithms; Desired Adjacency Graphs; Fuzzy Logic

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/344

001.#.#.#: 074.oai:ojs2.localhost:article/344

041.#.7.h: eng

520.3.#.a: 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.

773.1.#.t: Journal of Applied Research and Technology; Vol. 10 Núm. 6

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.16656423.2012.10.6.344

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

license_type: by-nc-sa

_deleted_conflicts: 2-7e6175f981cb8afe3ed3a76b9331a91d

No entro en nada

No entro en nada 2

Artículo

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

M. Sait, Sadiq; C. Oughali, F.; M. Arafeh, A.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

M. Sait, Sadiq, et al. (2012). FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm. Journal of Applied Research and Technology; Vol. 10 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45586

Descripción del recurso

Autor(es)
M. Sait, Sadiq; C. Oughali, F.; M. Arafeh, A.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm
Fecha
2012-12-01
Resumen
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.
Tema
EDA; FSM Synthesis; State Encoding; Simulated Evolution; Multiobjective Optimization; Non-Deterministic Algorithms; Desired Adjacency Graphs; Fuzzy Logic
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces