High-Dimensional Extension of the TICER Algorithm

被引:5
|
作者
Hao, Limin [1 ]
Shi, Guoyong [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Micro Nano Elect, Shanghai 200240, Peoples R China
关键词
Integrated circuit modeling; Approximation algorithms; Impedance; Passive networks; Capacitance; Symmetric matrices; Transfer functions; Driving point impedance (DPI); model order reduction; post-layout simulation; realizable RC reduction; time-constant equilibration reduction (TICER); REALIZABLE REDUCTION; MODEL; CIRCUITS; RC;
D O I
10.1109/TCSI.2021.3106390
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The TICER (TIme-Constant Equilibration Reduction) algorithm is a well-known resistor-capacitor (RC) network reduction algorithm. It finds wide applications in integrated circuit post-layout simulation tools. However, the original algorithm is one-dimensional in that each step eliminates one circuit node to obtain an approximately equivalent circuit by connecting additional elements to the neighboring nodes after each elimination. This work extends the TICER algorithm to its high-dimensional version in the sense that each step eliminates a subcircuit as a whole to obtain an approximately equivalent circuit, again by connecting extra elements to the neighboring nodes after each elimination. In practice the high-dimensional TICER (HD-TICER) algorithm finds many advantages over the classical one-dimensional TICER (1D-TICER) algorithm, which is a special case of the HD-TICER algorithm. An elegant mathematical derivation of the HD-TICER algorithm is provided by applying the notion of driving point impedance (DPI). The advantages of the HD-TICER algorithm are demonstrated by application to reductions of some purely resistive networks and RC networks. An approximate time constant estimation method is also provided for selection of a subblock circuit to eliminate.
引用
收藏
页码:4722 / 4734
页数:13
相关论文
共 50 条
  • [41] High-Dimensional Real Parameter Clonal Selection Memory Algorithm
    Song, Dan
    Fan, Xiaoping
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), VOL 1, 2016, : 42 - 46
  • [42] An optimal ADP algorithm for a high-dimensional stochastic control problem
    Nascimento, Juliana
    Powell, Warren
    2007 IEEE INTERNATIONAL SYMPOSIUM ON APPROXIMATE DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING, 2007, : 52 - +
  • [43] An oversampling algorithm for high-dimensional imbalanced learning with class overlapping
    Yang, Xu
    Xue, Zhen
    Zhang, Liangliang
    Wu, Jianzhen
    KNOWLEDGE AND INFORMATION SYSTEMS, 2025, 67 (02) : 1915 - 1943
  • [44] The xyz algorithm for fast interaction search in high-dimensional data
    Thanei, Gian-Andrea
    Meinshausen, Nicolai
    Shah, Rajen D.
    JOURNAL OF MACHINE LEARNING RESEARCH, 2018, 19
  • [45] Incremental gravitational search algorithm for high-dimensional benchmark functions
    Ozyon, Serdar
    Yasar, Celal
    Temurtas, Hasan
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (08): : 3779 - 3803
  • [46] A Fast Bacterial Swarming Algorithm For High-dimensional Function Optimization
    Chu, Ying
    Mi, Hua
    Liao, Huilian
    Ji, Zhen
    Wu, Q. H.
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 3135 - +
  • [47] AUTOMATIC CLUSTERING ALGORITHM AND ITS PROPERTIES IN HIGH-DIMENSIONAL SPACES
    MUCCIARDI, AN
    GOSE, EE
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1972, SMC2 (02): : 247 - +
  • [48] Supervised immune clonal evolutionary classification algorithm for high-dimensional
    Liu, Ruochen
    Zhang, Ping
    Jiao, Licheng
    Li, Yangyang
    NEUROCOMPUTING, 2012, 98 : 123 - 134
  • [49] Gray code permutation algorithm for high-dimensional data encryption
    Zanin, Massimiliano
    Pisarchik, Alexander N.
    INFORMATION SCIENCES, 2014, 270 : 288 - 297
  • [50] A High-dimensional Outlier Detection Algorithm Base on Relevant Subspace
    Gao, Zhipeng
    Zhao, Yang
    Niu, Kun
    Fan, Yidan
    2017 IEEE 15TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 15TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 3RD INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS(DASC/PICOM/DATACOM/CYBERSCI, 2017, : 1001 - 1008