Integrated Circuit Channel Routing using a Pareto-Optimal Genetic Algorithm

An important part of the integrated circuit design process is the channel routing stage, which determines how to interconnect components that are arranged in sets of rows. The channel routing problem has been shown to be NP-complete, thus this problem is often solved using genetic algorithms. The traditional objective for most channel routers is to minimize total area required to complete routing. However, another important objective is to minimize signal propagation delays in the circuit. This paper describes the development of a genetic channel routing algorithm that uses a Pareto-optimal approach to accommodate both objectives. When compared to the traditional channel routing approach, the new channel router produced layouts with decreased signal delay, while still minimizing routing area.

[1]  Rolf Drechsler,et al.  A hybrid genetic algorithm for the channel routing problem , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[2]  H. Eskandari,et al.  A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems , 2008, J. Heuristics.

[3]  Lalit M. Patnaik,et al.  A genetic algorithm for channel routing using inter-cluster mutation , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[4]  S. Singha,et al.  An Approach for Reducing Crosstalk in Restricted Channel Routing Using Graph Coloring Problem and Genetic Algorithm , 2008, 2008 International Conference on Computer and Electrical Engineering.

[5]  Takeshi Yoshimura,et al.  Efficient Algorithms for Channel Routing , 1982, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[6]  John P. Uyemura Introduction to VLSI Circuits and Systems , 2001 .

[7]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[8]  Samar Sen Sarma,et al.  A Mimetic Algorithm for Computing a Nontrivial Lower Bound on Number of Tracks in Two-Layer Channel Routing , 2007 .

[9]  Thomas G. Szymanski Dogleg Channel Routing is NP-Complete , 1985, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[11]  Alvaro Ruiz-Andino,et al.  Integration of Constraint Programming and Evolution Programs: Application to Channel Routing , 1998, IEA/AIE.

[12]  Lei Zhou,et al.  FPGA segmented channel routing using genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[13]  Peter J. Fleming,et al.  On the Evolutionary Optimization of Many Conflicting Objectives , 2007, IEEE Transactions on Evolutionary Computation.

[14]  S. Sitharama Iyengar,et al.  A general greedy channel routing algorithm , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[15]  Indrajit Ray,et al.  Optimal security hardening using multi-objective optimization on attack tree models of networks , 2007, CCS '07.

[16]  Xin Yao,et al.  Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions , 2005, Genetic Programming and Evolvable Machines.