License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ISAAC.2020.54
URN: urn:nbn:de:0030-drops-133988
URL: https://drops.dagstuhl.de/opus/volltexte/2020/13398/
Go to the corresponding LIPIcs Volume Portal


Uchizawa, Kei

Size, Depth and Energy of Threshold Circuits Computing Parity Function

pdf-format:
LIPIcs-ISAAC-2020-54.pdf (0.4 MB)


Abstract

We investigate relations among the size, depth and energy of threshold circuits computing the n-variable parity function PAR_n, where the energy is a complexity measure for sparsity on computation of threshold circuits, and is defined to be the maximum number of gates outputting "1" over all the input assignments. We show that PAR_n is hard for threshold circuits of small size, depth and energy:
- If a depth-2 threshold circuit C of size s and energy e computes PAR_n, it holds that 2^{n/(elog ^e n)} ≤ s; and
- if a threshold circuit C of size s, depth d and energy e computes PAR_n, it holds that 2^{n/(e2^{e+d}log ^e n)} ≤ s. We then provide several upper bounds:
- PAR_n is computable by a depth-2 threshold circuit of size O(2^{n-2e}) and energy e;
- PAR_n is computable by a depth-3 threshold circuit of size O(2^{n/(e-1)} + 2^{e-2}) and energy e; and
- PAR_n is computable by a threshold circuit of size O((e+d)2^{n-m}), depth d + O(1) and energy e + O(1), where m = max (((e-1)/(d-1))^{d-1}, ((d-1)/(e-1))^{e-1}). Our lower and upper bounds imply that threshold circuits need exponential size if both depth and energy are constant, which contrasts with the fact that PAR_n is computable by a threshold circuit of size O(n) and depth 2 if there is no restriction on the energy. Our results also suggest that any threshold circuit computing the parity function needs depth to be sparse if its size is bounded.

BibTeX - Entry

@InProceedings{uchizawa:LIPIcs:2020:13398,
  author =	{Kei Uchizawa},
  title =	{{Size, Depth and Energy of Threshold Circuits Computing Parity Function}},
  booktitle =	{31st International Symposium on Algorithms and Computation (ISAAC 2020)},
  pages =	{54:1--54:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-173-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{181},
  editor =	{Yixin Cao and Siu-Wing Cheng and Minming Li},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/13398},
  URN =		{urn:nbn:de:0030-drops-133988},
  doi =		{10.4230/LIPIcs.ISAAC.2020.54},
  annote =	{Keywords: Circuit complexity, neural networks, threshold circuits, sprase activity, tradeoffs}
}

Keywords: Circuit complexity, neural networks, threshold circuits, sprase activity, tradeoffs
Collection: 31st International Symposium on Algorithms and Computation (ISAAC 2020)
Issue Date: 2020
Date of publication: 04.12.2020


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI