title: 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.
authors.affiliation: IDSIA, Corso Elvezia 36, CH-6900, Lugano, Switzerland. nic@idsia.ch
authors.forename: N N
authors.initials: NN
authors.lastname: Schraudolph
mesh_terms: D000465:Algorithms; D016015:Logistic Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results