An optimized lookup-table for the evaluation of sigmoid function for artificial neural networks

In this paper, we present an efficient design of lookup-table (LUT) for the evaluation of hyperbolic tangent sigmoid to be used for the hardware implementation of artificial neural networks. Besides, we have suggested an LUT optimization scheme which maximizes the number of argument values in a sub-domain corresponding to each LUT word for a specified limit of accuracy. We have shown that the hardware-complexity of the proposed LUT implementation could be significantly reduced by using simplified combinational circuits for selective sign-conversion and efficient design of range decoder by logic subexpression sharing. From the synthesis results, we find that the proposed design involves comparable delay, but requires less than one-fourth of the area and area-delay complexity compared with the existing LUT-based implementations.

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