A fast, compact approximation of the exponential function.
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.
Schraudolph
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IDSIA, Corso Elvezia 36, CH-6900, Lugano, Switzerland. nic@idsia.ch
D000465:Algorithms; D016015:Logistic Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results