A Fast, Compact Approximation of the Exponential Function
Abstract
Neural network simulations often spend a large proportion of their time computing exponential functions. Since the exponentiation routines of typical math libraries are rather slow, their replacement with a fast approximation can greatly reduce the overall computation time. This article describes how exponentiation can be approximated by manipulating the components of a standard (IEEE-754) floating-point representation. This models the exponential function as well as a lookup table with linear interpolation, but is significantly faster and more compact.
Cite
Text
Schraudolph. "A Fast, Compact Approximation of the Exponential Function." Neural Computation, 1999. doi:10.1162/089976699300016467Markdown
[Schraudolph. "A Fast, Compact Approximation of the Exponential Function." Neural Computation, 1999.](https://mlanthology.org/neco/1999/schraudolph1999neco-fast/) doi:10.1162/089976699300016467BibTeX
@article{schraudolph1999neco-fast,
title = {{A Fast, Compact Approximation of the Exponential Function}},
author = {Schraudolph, Nicol N.},
journal = {Neural Computation},
year = {1999},
pages = {853-862},
doi = {10.1162/089976699300016467},
volume = {11},
url = {https://mlanthology.org/neco/1999/schraudolph1999neco-fast/}
}