Automatic design of approximate circuits by means of multi-objective evolutionary algorithms

Recently, power efficiency has become the most important parameter of many real circuits. At the same time, a wide range of applications capable of tolerating imperfections has spread out especially in multimedia. Approximate computing, an emerging paradigm, takes advantage of relaxed functional requirements to make computer systems more efficient in terms of energy consumption, speed or complexity. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a design method based on a multi-objective evolutionary algorithm is proposed. For a given circuit, the method is able to produce a set of Pareto optimal solutions in terms of the error, power consumption and delay. The proposed design method uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm for design space exploration. The method is used to design Pareto optimal approximate versions of arithmetic circuits such as multipliers and adders.

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