A fast, compact approximation of the exponential function (1999) [13 citations — 4 self]
by Nicol N. Schraudolph
Neural Computation
ftp://ftp.idsia.ch/pub/nic/exp.ps.gz
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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. 1
Citations
| 58 | for Binary Floating-Point Arithmetic, ANSI/IEEE Standard 754-1985, The Institute of Electrical and – IEEE - 1985 |
| 52 | On the Lambert W function – Corless, Gonnet, et al. - 1996 |
| 12 | Algorithm 443: solution of the transcendental equation we w = x – Fritsch, Shafer, et al. - 1973 |
