## A Fast, Compact Approximation of the Exponential Function

N. N. Schraudolph. A Fast, Compact Approximation of the Exponential Function. Neural Computation, 11(4):853–862, 1999.

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### 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 paper 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.

### BibTeX Entry

```@article{Schraudolph99,
author = {Nicol N. Schraudolph},
title = {\href{http://nic.schraudolph.org/pubs/Schraudolph99.pdf}{
A Fast, Compact Approximation of the Exponential Function}},
pages = {853--862},
journal = {\href{http://neco.mitpress.org/}{Neural Computation}},
volume =  11,
number =  4,
year =  1999,
b2h_type = {Journal Papers},
b2h_topic = {Other},
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
paper 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.
}}
```

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