Genetic Algorithm Based Multiobjective Optimization for Very Large-Scale Integration (Vlsi) Circuit Partitioning - Roy, Sharadindu (International Journal of Computer Sciences and Engineering, 2019)
A genetic algorithm based multi objective optimization technique for very large-scale integration (VLSI) circuit partitioning has been proposed. An efficient fitness function that simultaneously optimizes minimum net cut size and delay time and maximum sleep time has been worked out along with minimum power consumption. Use of bipartition has balanced the circuit perfectly. Circuit partitioning is a non-polynomial (NP) hard problem. I have used Genetic algorithm (GA)-based optimization as it shows a global optimum solution. This is a hyper graph - based solution. Since it is a part of a physical design, all the computational part including input-output (IO) pads are converted into a hyper graph. Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural selection. Fitness value has been evaluated and solution with low fitness value has been discarded. The method has been applied on the net list files used in ISPD’98 circuit benchmark suite where each file contained 20-30 nodes. MATLAB18a was used to code all the algorithms. The improvement of net cut size, delay and sleep time was 40.62%, 41.54% and 95.42% respectively compared to initial bipartition of circuit. Thus, the proposed methodology might be promising for current trends in VLSI circuit partitioning.