Publication Details
Extensions of Cartesian Genetic Programming for Optimization of Complex Combinational Circuits
logic optimization, genetic programming, digital circuit
Evolution and optimization of digital circuits using standard Cartesian Genetic
Programming (CGP) is not scalable mainly because the evaluation time grows
exponentially with increasing number of circuit inputs. We propose to combine two
extensions of CGP to improve post-synthesis optimization capabilities of CGP.
Firstly, we replace the standard fitness function by an equivalence checking
algorithm which significantly reduces the fitness evaluation time for complex
circuits. Secondly, we propose to modify the selection strategy of CGP to
increase the number of functionally correct solutions that can be created using
a mutation operator. Proposed extensions of CGP are evaluated using the LGSynth93
benchmark circuits. Experimental results show that extended CGP can significantly
reduce the number of gates (area reduced by 24% on average) in benchmark circuits
for the cost of runtime in comparison to conventional methods such as SIS and
ABC.
@inproceedings{BUT192752,
author="Zdeněk {Vašíček} and Lukáš {Sekanina}",
title="Extensions of Cartesian Genetic Programming for Optimization of Complex Combinational Circuits",
booktitle="Proc. of the 20th International Workshop on Logic and Synthesis",
year="2011",
pages="55--61",
publisher="University of California San Diego",
address="San Diego"
}