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Efficient Finite Word Length Determination For Neural Networks Implementation

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3 Author(s)
D. Emir ; National Engineering School of Tunis, Tunisia ; B. Abdellatif ; B. Ammar

Most of the artificial neural networks (ANN) based applications are implemented on FPGAs using fixed-point arithmetic. The problem is to achieve a balance between the need for numerical precision, which is important for network accuracy, and the cost of logic areas, i.e. FPGA resources. In this paper we propose a genetic algorithm based methodology permitting the optimization of the FPGA resources needed for the implementation of a pipelined recurrent neural network (PRNN) while respecting the precision constraints. The quality of our methodology would be evaluated through experiment on a PRNN based WCDMA receiver. Our methodology is not restricted to this class of ANNs and can be used for any complex with variable dimensions system

Published in:

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06)  (Volume:2 )

Date of Conference:

28-30 Nov. 2005