Created by W.Langdon from gp-bibliography.bib Revision:1.7546
Approximate computing, an emerging paradigm in computer engineering, takes advantage of relaxed functionality requirements to make computer systems more efficient in terms of energy consumption, computing performance or complexity. Error resilient applications can achieve significant savings while still serving their purpose with the same or a slightly degraded quality.
Even though new design methods for approximate computing are emerging, there is alack of methods for automated approximate HW/SW design offering a rich set of compromise solutions. Conventional methods often produce solutions that are far from an optimum. Evolutionary algorithms have been shown to bring innovative solutions to complex design and optimization problems. However, these methods suffer from several problems,such as the scalability or a high number of fitness evaluations needed to evolve competitive results. Finally, existing methods are usually single-objective whilst multi-objective approach is more suitable in the case of approximate computing.
In this thesis, a new automated multi-objective parallel evolutionary algorithm for circuit design and approximation is proposed. The method is based on Cartesian Genetic Programming. In order to improve the scalability of the algorithm, a brand new highly parallel implementation was proposed. The principles of the NSGA-II algorithm were used to provide the multiobjective design and approximation capability.
The performance of the implementation was evaluated in multiple different applications,in particular (approximate) combinational arithmetic circuits design, bent Boolean functions discovery and approximate logic circuits for TMR schema. In these cases, important improvements with respect to the state of the art were obtained.",
Genetic Programming entries for Radek Hrbacek