ML Anthology

The Effects of Precision Constraints in a Backpropagation Learning Network

Paul W. Hollis, John S. Harper, John J. Paulos
NeCo 1990 pp. 363-373
doi:10.1162/NECO.1990.2.3.363 /neco/1990/hollis1990neco-effects/

Abstract

This paper presents a study of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed point arithmetic to implement the backpropagation algorithm.

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Cite

Text

Hollis et al. "The Effects of Precision Constraints in a Backpropagation Learning Network." Neural Computation, 1990. doi:10.1162/NECO.1990.2.3.363

Markdown

[Hollis et al. "The Effects of Precision Constraints in a Backpropagation Learning Network." Neural Computation, 1990.](https://mlanthology.org/neco/1990/hollis1990neco-effects/) doi:10.1162/NECO.1990.2.3.363

BibTeX

@article{hollis1990neco-effects,
  title     = {{The Effects of Precision Constraints in a Backpropagation Learning Network}},
  author    = {Hollis, Paul W. and Harper, John S. and Paulos, John J.},
  journal   = {Neural Computation},
  year      = {1990},
  pages     = {363-373},
  doi       = {10.1162/NECO.1990.2.3.363},
  volume    = {2},
  url       = {https://mlanthology.org/neco/1990/hollis1990neco-effects/}
}