PROJECT TITLE :
Evolutionary Approach to Approximate Digital Circuits Design - 2017
In approximate computing, the need of excellent functional behavior will be relaxed as a result of some applications are inherently error resilient. Approximate circuits, that fall into the approximate computing paradigm, are designed in such a manner that they are doing not absolutely implement the logic behavior given by the specification and, hence, their accuracy will be exchanged for lower space, delay or power consumption. In order to automate the look process, we tend to propose to evolve approximate digital circuits that show a minimal error for a equipped amount of resources. The look process, which is based on Cartesian genetic programming (CGP), will be repeated several times in order to obtain various tradeoffs between the accuracy and area. A heuristic seeding mechanism is introduced to CGP, which permits for improving not solely the quality of evolved circuits, however additionally reducing the time of evolution. The potency of the proposed technique is evaluated for the gate further as the purposeful level evolution. In particular, approximate multipliers and median circuits that show very good parameters compared with alternative on the market implementations were made by means that of the proposed methodology.
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