SABSA: SWITCHING-ACTIVITY-BASED STATE ASSIGNMENT

1994 ◽  
Vol 05 (02) ◽  
pp. 203-212 ◽  
Author(s):  
SCOTT WASHABAUGH ◽  
PAUL D. FRANZON ◽  
H. TROY NAGLE

Many algorithms have been proposed for the state assignment of Finite-State Machines. The usual goal of these algorithms is to reduce the area required to implement these machines. This paper discusses SABSA, an algorithm whose goal is to reduce the number of state bit transitions in the operating environment. SABSA does this by considering measured transition probabilities between states. When combined with a technology such as CMOS, this leads to lower power consumption. A RISC controller was taken from high level design to transistor level layout, and a power reduction of 35% was obtained with this new state assignment algorithm.

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

VLSI Design ◽  
1994 ◽  
Vol 2 (1) ◽  
pp. 81-88 ◽  
Author(s):  
R. Z. Makki ◽  
S. Su

In this paper, we study the problem of state assignment as it relates to silicon area, propagation delay time and testability of finite state machines. The results of a study involving various FSM benchmarks show that the simple technique of one-hot encoding often produces better results than those attained by complex state assignment algorithms.


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