Efficient Solution of Language Equations Using Partitioned Representations

Authors:
Alan Mishchenko;Robert Brayton;Roland Jiang;Tiziano Villa;Nina Yevtushenko
Affiliations:
University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of Udine, Italy;Tomsk State University, Russia
Venue:
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Year:
2005

Citing 4
Cited 2

The maximum set of permissible behaviors for FSM networks

ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Non-linear quantification scheduling in image computation

Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Multi Component Digital Circuit Optimization by Solving FSM Equations

DSD '03 Proceedings of the Euromicro Symposium on Digital Systems Design
The Compositional Far Side of Image Computation

Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design

A new algorithm for the largest compositionally progressive solution of synchronous language equations

Proceedings of the 17th ACM Great Lakes symposium on VLSI
Compositionally Progressive Solutions of Synchronous FSM Equations

Discrete Event Dynamic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A class of discrete event synthesis problems can be reduced to solving language equations f 驴 X 驴 S, where F is the fixed component and S the specification. Sequential synthesis deals with FSMs when the automata for F and S are prefix closed, and are naturally represented by multi-level networks with latches. For this special case, we present an efficient computation, using partitioned representations, of the most general prefix-closed solution of the above class of language equations. The transition and the output relations of the FSMs for F and S in their partitioned form are represented by the sets of output and next state functions of the corresponding networks. Experimentally, we show that using partitioned representations is much faster than using monolithic representations, as well as applicable to larger problem instances.