Self Modifying Finite Automata (SMFA) based State Machine Implementation for Lower Energy

被引:1
|
作者
Keung, Ka-Ming [1 ]
Tyagi, Akhilesh [1 ]
机构
[1] Iowa State Univ, Ames, IA 50011 USA
关键词
D O I
10.1109/ACSSC.2008.5074804
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Many finite state machines (FSMs) in embedded systems exhibits strong locality in state transitions. Traditional state assignment algorithms consider all FSM states' outputs and next states to assign a code for each state. Self-modifying finite state machine(SM-FSM) takes the FSM strong locality characteristics to dynamically change the traditional Finite State Machine during runtime. This not only reduces the FSM search space, but also reduces the code space and allows code sharing among the dynamic behaviors. This paper proposes an architecture to support SMFA and evaluates the gain from SMFA on state machines with different characteristic.
引用
收藏
页码:2103 / 2107
页数:5
相关论文
共 50 条
  • [21] Using finite state automata to produce self-optimization and self-control
    Tung, B
    Kleinrock, L
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 1996, 7 (04) : 439 - 448
  • [22] A Cellular Automata Guided Obfuscation Strategy For Finite-State-Machine Synthesis
    Karmakar, Rajit
    Jana, Suman Sekhar
    Chattopadhyay, Santanu
    PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC), 2019,
  • [23] Energy consumption modeling of machining transient states based on finite state machine
    Shun Jia
    Renzhong Tang
    Jingxiang Lv
    Qinghe Yuan
    Tao Peng
    The International Journal of Advanced Manufacturing Technology, 2017, 88 : 2305 - 2320
  • [24] Energy consumption modeling of machining transient states based on finite state machine
    Jia, Shun
    Tang, Renzhong
    Lv, Jingxiang
    Yuan, Qinghe
    Peng, Tao
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (5-8): : 2305 - 2320
  • [25] Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks
    Cabessa, Jeremie
    Sima, Jiri
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I, 2019, 11727 : 806 - 818
  • [26] A theory of computation based on unsharp quantum logic: Finite state automata and pushdown automata
    Shang, Yun
    Lu, Xian
    Lu, Ruqian
    THEORETICAL COMPUTER SCIENCE, 2012, 434 : 53 - 86
  • [27] Finite state machine implementation for left ventricle modeling and control
    King, Jacob M.
    Bergeron, Clint A.
    Taylor, Charles E.
    BIOMEDICAL ENGINEERING ONLINE, 2019, 18 (1)
  • [28] A study of finite state machine coding styles for implementation in FPGAs
    Rafla, Nader I.
    Davis, Brett LaVoy
    IEEE MWSCAS'06: PROCEEDINGS OF THE 2006 49TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS,, 2006, : 337 - +
  • [29] The virtual finite-state machine design and implementation paradigm
    FloraHolmquist, AR
    Morton, E
    OGrady, JD
    Staskauskas, MG
    BELL LABS TECHNICAL JOURNAL, 1997, 2 (01) : 96 - 113
  • [30] EFFICIENT TABLE-DRIVEN IMPLEMENTATION OF THE FINITE STATE MACHINE
    HARRISON, PG
    JOURNAL OF SYSTEMS AND SOFTWARE, 1981, 2 (03) : 201 - 211