Minimization of mealy finite-state machines by using the values of the output variables for state assignment

被引:13
|
作者
Solov'ev, V. V. [1 ]
机构
[1] Bialystok Tech Univ, Bialystok, Poland
关键词
D O I
10.1134/S1064230717010129
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Structural models of finite-state machines (FSMs) that make it possible to use the values of the output variables for encoding the internal states are studied. To minimize the area (the parameter area is used to denote cost in the context of this paper) of FSM implementation, it is proposed to use the structural model of the class D FSM. A method for the design of the class D FSM in FPGA is proposed. This method involves two phases-splitting the internal states of the FSM (to satisfy the necessary conditions for the construction of the class D FSM) and encoding the internal states (to ensure that the codes are mutually orthogonal). It is shown that the proposed method reduces the area of FSM implementation for all families of FPGAs of various manufacturers by a factor of 1.41-1.72 on average and by a factor of two for certain families. Practical issues concerning the method and the specific features of its use are discussed, and possible directions of the elaboration of this approach are proposed.
引用
收藏
页码:96 / 104
页数:9
相关论文
共 50 条
  • [41] In vitro implementation of finite-state machines
    Garzon, M
    Gao, Y
    Rose, JA
    Murphy, RC
    Deaton, R
    Franceschetti, DR
    Stevens, SE
    AUTOMATA IMPLEMENTATION, 1998, 1436 : 56 - 74
  • [42] Abstractions of random finite-state machines
    Oikonomou, KN
    FORMAL METHODS IN SYSTEM DESIGN, 2001, 18 (03) : 171 - 207
  • [43] Model matching for finite-state machines
    Di Benedetto, MD
    Sangiovanni-Vincentelli, A
    Villa, T
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2001, 46 (11) : 1726 - 1743
  • [44] CHEMICAL IMPLEMENTATION OF FINITE-STATE MACHINES
    HJELMFELT, A
    WEINBERGER, ED
    ROSS, J
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1992, 89 (01) : 383 - 387
  • [45] Abstractions of Random Finite-State Machines
    Kostas N. Oikonomou
    Formal Methods in System Design, 2001, 18 : 171 - 207
  • [46] Training Linear Finite-State Machines
    Ardakani, Arash
    Ardakani, Amir
    Gross, Warren J.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [47] Product Construction of Finite-State Machines
    Hsieh, Samuel C.
    WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, VOLS 1 AND 2, 2010, : 141 - 143
  • [48] CASCADE SYNTHESIS OF FINITE-STATE MACHINES
    ZEIGER, HP
    INFORMATION AND CONTROL, 1967, 10 (04): : 419 - &
  • [49] OPTIMAL STATE ASSIGNMENT FOR FINITE STATE MACHINES
    DEMICHELI, G
    BRAYTON, RK
    SANGIOVANNIVINCENTELLI, A
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 1985, 4 (03) : 269 - 285
  • [50] A state assignment algorithm for finite state machines
    Skias, D
    Haniotakis, T
    Tsiatouhas, Y
    Arapoyanni, A
    ICECS 2000: 7TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS & SYSTEMS, VOLS I AND II, 2000, : 823 - 826