Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits

Abstract

Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimization techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesizer (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms.