scholarly journals Improving Characteristics of LUT-Based Mealy FSMs with Twofold State Assignment

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 901
Author(s):  
Alexander Barkalov ◽  
Larysa Titarenko ◽  
Kazimierz Krzywicki ◽  
Svetlana Saburova

Practically, any digital system includes sequential blocks. This article is devoted to a case when sequential blocks are represented by models of Mealy finite state machines (FSMs). The performance (maximum operating frequency) is one of the most important characteristics of an FSM circuit. In this article, a method is proposed which aims at increasing the operating frequency of LUT-based Mealy FSMs with twofold state assignment. This is done using only extended state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed approach leads to LUT-based Mealy FSM circuits having two levels of logic blocks. Each function for any logic level is represented by a circuit including a single LUT. The proposed method is illustrated by an example of synthesis. The results of experiments conducted with standard benchmarks show that the proposed approach produces LUT-based circuits with significantly higher operating frequency than it is for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, twofold state assignment). The performance is increased by an average of 15.9 to 25.49 percent. These improvements are accompanied by a small growth of the numbers of LUTs compared with circuits based on twofold state assignment. Our approach provides the best area-time products compared with other investigated methods. The advantages of the proposed approach increase as the number of FSM inputs and states increases.

VLSI Design ◽  
1994 ◽  
Vol 2 (2) ◽  
pp. 105-116
Author(s):  
S. Muddappa ◽  
R. Z. Makki ◽  
Z. Michalewicz ◽  
S. Isukapalli

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.


1994 ◽  
Vol 30 (8) ◽  
pp. 627-629 ◽  
Author(s):  
S.K. Hong ◽  
I.C. Park ◽  
C.M. Kyung ◽  
S.H. Hwang

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