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Related Experiment Video

Updated: Jul 3, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

A fast, streaming SIMD Extensions 2, logistic squashing function.

J J Milner1, A J Grandison

  • 1School of Computing and Mathematical Sciences, University of Greenwich, Greenwich, London, U.K. cplusplus@hotmail.co.uk

Neural Computation
|July 16, 2008
PubMed
Summary
This summary is machine-generated.

This study optimizes the logistic squashing function for neural networks using Intel

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Last Updated: Jul 3, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

  • Computer Science
  • Computational Mathematics

Background:

  • The logistic squashing function is crucial for neural network performance.
  • Schraudolph's exponential approximation enhanced this function's efficiency.

Purpose of the Study:

  • To apply Intel's Streaming SIMD Extensions 2 (SSE2) to Schraudolph's approximation.
  • To further accelerate the logistic squashing function for neural network applications.

Main Methods:

  • Implementation of a 32-bit logistic squashing function using SSE2 instructions.
  • Performance benchmarking on an Intel Pentium D 3.6 GHz CPU.

Main Results:

  • The new SSE2 logistic squashing function achieved significant speedups.
  • Calculations were up to 38 times faster than conventional exponential functions.
  • It was up to 16 times faster than Schraudolph's 32-bit method.

Conclusions:

  • SSE2 significantly boosts the performance of logistic squashing functions.
  • This optimized function offers substantial computational advantages for neural networks.