Parallel Multi-Objective Evolutionary Design of Approximate Circuits

Cartesian Genetic Programming, Parallel Evolutionary Al- gorithms, Multi-objective Optimization, Cluster, Combina- tional Circuit Design, Approximate Circuits

Evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multiobjective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multiobjective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.