Constrained Cartesian Genetic Programming - A New Paradigm for Evolving Imprecise Multipliers

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Optimization of multiplier architecture at the gate level is essential to reduce the silicon area consumed by the hardware for various low power applications. Further the conventional optimization algorithms like Cartesian Genetic Programming (CGP) is more suitable for evolving combinational circuits both at the gate-level and at the functional-level. We propose Constrained Cartesian Genetic Programming (CCGP), a variant of CGP to evolve various architectures of digital arithmetic circuits, especially multipliers. The proposed methodology CCGP is more suitable for evolving both imprecise and precise multipliers and has got various performance benefits over the other existing optimization methods. In this paper we make an exhaustive approach for the imprecise multiplier problem and we analyze the CCGP for its performances.
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Evolutionary Computation; Cartesian Genetic Programming; Imprecise Computation; Low Power Arithmetic; Recursive Multiplier

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J. F. Miller, D. Job and V. K. Vassilev., ‘Principles in the Evolutionary Design of Digital Circuits - Part l’, Genetic Programming and Evolvable Machines, vol. 1, no. 1, pp. 8-35., 2000

Vassilev, V.K.; Job, D.; Miller, J.F., ‘Towards the automatic design of more efficient digital circuits’, Evolvable Hardware,. Proceedings. The Second NASA/DoD Workshop on Evolvable Hardware, vol., no., pp.151-160., 2000.

James Alfred Walker and Julian Francis Miller., ‘Improving the evolvability of digital multipliers using embedded cartesian genetic programming and product reduction’, in Proceedings of the 6th international conference on Evolvable Systems: from Biology to Hardware (ICES'05), 2005

James Alfred Walker, Katharina V, Stephen L. Smith, and Julian Francis Miller., ‘Parallel evolution using multi-chromosome cartesian genetic programming’, Genetic Programming and Evolvable Machines, 10, 417-445, 2009.

Harding, S.; Miller, J.F.; Banzhaf, W., ‘Self modifying Cartesian Genetic Programming: Parity’, CEC '09. IEEE Congress on Evolutionary Computation, vol., no., pp.285-292, 18-21, 2009

Walker, J.A.; Miller, J.F., ‘The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming’, IEEE Transactions on Evolutionary Computation, vol.12, no.4, pp.397-417, 2008.

Lones, M and Smith, S.L., ‘Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming’, Genetic and Evolutionary Computation: Medical Applications, Wiley, 2010.

J.R. Koza, ‘Genetic Programming: On the Programming of Computers by Means of Natural Selection’, (MIT Press, Cambridge, 1992).

Kunaraj. K.; Seshasayanan. R., "Leading one detectors and leading one position detectors - An evolutionary design methodology," Canadian Journal of Electrical and Computer Engineering, vol.36, no.3, pp.103,110, Summer 2013.

Kunaraj Kumarasamy and Seshasayanan Ramachandran, "Leading One Detectors: Evolutionary Approach", Arabian Gulf Journal of Scientific Research, AGJSR 31(2/3), pp.145-153, 2013.

R. K. Jena, P. Srivastava, G. K. Sharma, A Review on Genetic Algorithm in Parallel & Distributed Environment, (2008) International Review on Computers and Software (IRECOS), 3. (5), pp. 532-544.

Rose, A.V.V., Ramachandran, R.S., Genetic algorithm based optimization of vertical links for efficient 3D NoC multicore crypto processor, (2013) International Review on Computers and Software (IRECOS), 8 (5), pp. 1082-1090.


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