Optimal state assignment of sequential circuits using a genetic local search with flexible cost functions

In this dissertation, we solve the finite state machine (FSM) state assignment problem using an implementation of a genetic local search algorithm (GLS) that selectively targets design goals with the use of flexible cost functions. Our GLS quickly converges toward a globally optimal state encoding, producing high quality solutions. We explore the properties of encoding-based (calculated from the encoding values) and synthesis-based (calculated from synthesis results) cost functions. With these functions, the GLS consistently finds the best known encoding solutions with FSMs of less than sixty states and very good assignments for larger benchmarks.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Olson, Eric Peter

Contributors dc:contributor

• Kang, Sung Mo

• Engineering, Electronics and Electrical
• Computer Science

Statement dc:rights

• Copyright 1995 Olson, Eric Peter

Language dc:language

eng

Identifier

(UMI)AAI9702630
9780591089530
AAI9702630

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/21498