Symmetric table addition methods for neural network approximations

Nihal Koc-Sahan; Jason Schlessman; Michael J. Schulte

doi:10.1117/12.448641

20 November 2001 Symmetric table addition methods for neural network approximations

Nihal Koc-Sahan, Jason Schlessman, Michael J. Schulte

Proceedings Volume 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI; (2001) https://doi.org/10.1117/12.448641
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

Symmetric table addition methods (STAMs) approximate functions by performing parallel table lookups, followed by multioperand addition. STAMs require significantly less memory than direct table lookups and are faster than piecewise linear approximations. This paper investigates the application of STAMs to the sigmoid function and its derivative, which are commonly used in artificial neural networks. Compared to direct table lookups, STAMs require between 23 and 41 times less memory for sigmoid and between 24 and 46 times less memory for sigmoid's derivative, when the input operand size is 16 bits and the output precision is 12 bits.

Citation Download Citation

Nihal Koc-Sahan, Jason Schlessman, and Michael J. Schulte "Symmetric table addition methods for neural network approximations", Proc. SPIE 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI, (20 November 2001); https://doi.org/10.1117/12.448641

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