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

Low Power Synthesis of Finite State Machines — State Assignment Decomposition Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 1850041 ◽  
Author(s):  
Krzysztof Kajstura ◽  
Dariusz Kania
Keyword(s):  
Power Consumption ◽  
Finite State Machine ◽  
Reducing Power ◽  
Finite State Machines ◽  
Theoretical Background ◽  
State Machines ◽  
State Assignment ◽  
Probabilistic Description ◽  
Finite State ◽  
Low Power Synthesis

A new method for reducing power consumption in finite state machines (FSMs) is proposed. Probabilistic description of the FSMs is the theoretical background of power-oriented state assignment. The algorithm of state assignment is based on a decomposition strategy of coding. This idea uses a binary tree in which nodes are created by sharing a finite state machine. The algorithm has been applied to the LGSynth91 benchmark and has also been compared to other approaches. The experiments showed that the proposed method leads to a reduction in power consumption compared to the state encoding algorithms that have already been developed. Reduction of the circuits’ area is also observed.

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.

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