State assignment and logic optimization for finite state machines

2009 ◽  
Vol 42 (1) ◽  
pp. 39-44
Author(s):  
Robert Czerwinski ◽  
Dariusz Kania
Keyword(s):  
Finite State Machines ◽  
State Machines ◽  
State Assignment ◽  
Logic Optimization ◽  
Finite State

Synthesis method of high speed finite state machines

2010 ◽  
Vol 58 (4) ◽  
pp. 635-644 ◽  
Author(s):  
R. Czerwiński ◽  
D. Kania
Keyword(s):  
High Speed ◽  
Synthesis Method ◽  
Finite State Machines ◽  
Optimization Method ◽  
Transition Function ◽  
The State ◽  
State Machines ◽  
State Assignment ◽  
Logic Optimization ◽  
Finite State

Synthesis method of high speed finite state machines The paper is concerned with the problem of state assignment and logic optimization of high speed finite state machines. The method is designed for PAL-based CPLDs implementations. Determining the number of logic levels of the transition function before the state encoding process, and keeping the constraints during the process is the main problem at hand. A number of coding bits, as well as codes for the states, are adjusted to achieve a machine with a determined number of logic levels. Elements of two-level minimization are taken into consideration in the state assignment. The proposed optimization method is based on utilizing tri-state buffers, thus enabling achievement of a one-logic-level output block.

scholarly journals Pioneer: A New Tool for Coding of Multi-Level Finite State Machines Based on Evolution Programming

VLSI Design ◽  
1994 ◽  
Vol 2 (2) ◽  
pp. 105-116
Author(s):  
S. Muddappa ◽  
R. Z. Makki ◽  
Z. Michalewicz ◽  
S. Isukapalli
Keyword(s):  
Finite State Machines ◽  
Mutation Rates ◽  
Natural Phenomenon ◽  
State Machines ◽  
State Assignment ◽  
Wide Range ◽  
Finite State ◽  
Multi Level ◽  
Crossover And Mutation ◽  
Evolution Programs

In this paper we present a new tool for the encoding of multi-level finite state machines based on the concept of evolution programming. Evolution programs are stochastic adaptive algorithms, based on the paradigm of genetic algorithms whose search methods model some natural phenomenon: genetic inheritance and Darwinian strife for survival. Crossover and mutation rates were tailored to the state assignment problem experimentally. We present results over a wide range of MCNC benchmarks which demonstrate the effectiveness of the new tool. The results show that evolution programs can be effectively applied to state assignment.

State assignment in finite state machines for minimal switching power consumption

1994 ◽  
Vol 30 (8) ◽  
pp. 627-629 ◽  
Author(s):  
S.K. Hong ◽  
I.C. Park ◽  
C.M. Kyung ◽  
S.H. Hwang
Keyword(s):  
Power Consumption ◽  
Finite State Machines ◽  
State Machines ◽  
State Assignment ◽  
Switching Power ◽  
Finite State
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