Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 15, 1999, Vol. 11, No. 4, Pages 853-862
(doi: 10.1162/089976699300016467)
© 1999 Massachusetts Institute of Technology
A Fast, Compact Approximation of the Exponential Function
Article PDF (182.95 KB)
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.