Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and p...Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and power obtains much concentration. In this paper, a multi-objective greedy randomized adaptive search procedure (GRASP) is presented for simultaneous cutsize and circuit delay minimization. Each objective is assigned a preference or weight to direct the search procedure and generate a variety of efficient solutions by changing the preference. To get a good initial partition with minimal cutsize and circuit delay, the gain of each module in a circuit is computed by considering both signal nets and circuit delay. The performance of the proposed algorithm is evaluated on a standard set of partitioning benchmark. The experimental results show that the proposed algorithm can generate a set of Pareto optimal solutions and is efficient for tackling multi-objective circuit partitioning.展开更多
Very large scale integration （VLSI） circuit par- titioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combi- national optimization problem. In this paper, an e...Very large scale integration （VLSI） circuit par- titioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combi- national optimization problem. In this paper, an effective hy- brid multi-objective partitioning algorithm, based on discrete particle swarm optimzation （DPSO） with local search strat- egy, called MDPSO-LS, is presented to solve the VLSI two- way partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random two-point exchange operators are designed to avoid the case of generating infeasible so- lutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, Fiduccia-Matteyses （FM） strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on IS- CAS89 benchmark circuits show that the proposed algorithm is efficient.展开更多
基金National Natural Science Foudation of China (No. 61070020 )Research Foundation for Doctoral Program of Ministry of Education,China (No. 20093514110004)Foundations of Education Department of Fujian Province,China (No. JA10284,No. JB07283)
文摘Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and power obtains much concentration. In this paper, a multi-objective greedy randomized adaptive search procedure (GRASP) is presented for simultaneous cutsize and circuit delay minimization. Each objective is assigned a preference or weight to direct the search procedure and generate a variety of efficient solutions by changing the preference. To get a good initial partition with minimal cutsize and circuit delay, the gain of each module in a circuit is computed by considering both signal nets and circuit delay. The performance of the proposed algorithm is evaluated on a standard set of partitioning benchmark. The experimental results show that the proposed algorithm can generate a set of Pareto optimal solutions and is efficient for tackling multi-objective circuit partitioning.
文摘Very large scale integration （VLSI） circuit par- titioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combi- national optimization problem. In this paper, an effective hy- brid multi-objective partitioning algorithm, based on discrete particle swarm optimzation （DPSO） with local search strat- egy, called MDPSO-LS, is presented to solve the VLSI two- way partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random two-point exchange operators are designed to avoid the case of generating infeasible so- lutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, Fiduccia-Matteyses （FM） strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on IS- CAS89 benchmark circuits show that the proposed algorithm is efficient.
