Transistor Sizing Using Particle Swarm Optimisation

被引:1
|
作者
White, Lyndon [1 ]
While, Lyndon [2 ]
Deeks, Ben [2 ]
Boussaid, Farid [1 ]
机构
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA, Australia
[2] Univ Western Australia, Sch Comp Sci & Software Engn, Perth, WA, Australia
关键词
D O I
10.1109/SSCI.2015.46
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We describe an application of particle swarm optimisation to the problem of determining the optimal sizing of transistors in an integrated circuit. The algorithm minimises the total area of silicon utilised by a given circuit, whilst maintaining the propagation delay of the circuit within a hard limit. It assesses designs using the well-known circuit simulation engine SPICE, making allowance for the inability of SPICE to assess poorly-designed circuits within a reasonable timeframe. Experiments on three different types of circuits demonstrate that the algorithm is able to derive excellent designs for a range of problem instances, including several problems where the Monte Carlo method is unable to find any feasible solutions at all.
引用
收藏
页码:259 / 266
页数:8
相关论文
共 50 条
  • [41] Particle swarm optimisation for dynamic optimisation problems: a review
    Jordehi, Ahmad Rezaee
    [J]. NEURAL COMPUTING & APPLICATIONS, 2014, 25 (7-8): : 1507 - 1516
  • [42] Particle swarm optimisation for discrete optimisation problems: a review
    Ahmad Rezaee Jordehi
    Jasronita Jasni
    [J]. Artificial Intelligence Review, 2015, 43 : 243 - 258
  • [43] Particle swarm optimisation for dynamic optimisation problems: a review
    Ahmad Rezaee Jordehi
    [J]. Neural Computing and Applications, 2014, 25 : 1507 - 1516
  • [44] Particle swarm optimisation for discrete optimisation problems: a review
    Jordehi, Ahmad Rezaee
    Jasni, Jasronita
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2015, 43 (02) : 243 - 258
  • [45] Placement and Sizing of D-STATCOM Using Particle Swarm Optimization
    Devi, S.
    Geethanjali, M.
    [J]. POWER ELECTRONICS AND RENEWABLE ENERGY SYSTEMS, 2015, 326 : 941 - 951
  • [46] Stochastic stability of particle swarm optimisation
    Adam Erskine
    Thomas Joyce
    J. Michael Herrmann
    [J]. Swarm Intelligence, 2017, 11 : 295 - 315
  • [47] CriPS: Critical Particle Swarm Optimisation
    Erskine, Adam
    Herrmann, J. Michael
    [J]. ECAL 2015: THE THIRTEENTH EUROPEAN CONFERENCE ON ARTIFICIAL LIFE, 2015, : 207 - 214
  • [48] Preserving diversity in particle swarm optimisation
    Hendtlass, T
    [J]. DEVELOPMENTS IN APPLIED ARTIFICIAL INTELLIGENCE, 2003, 2718 : 31 - 40
  • [49] Division of Labor in Particle Swarm Optimisation
    Vesterstrom, JS
    Riget, J
    Krink, T
    [J]. CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2002, : 1570 - 1575
  • [50] Adaptive multifactorial particle swarm optimisation
    Tang, Zedong
    Gong, Maoguo
    [J]. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2019, 4 (01) : 37 - 46