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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 numeric 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...
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