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  • 标题:Size, Depth and Energy of Threshold Circuits Computing Parity Function
  • 本地全文:下载
  • 作者:Kei Uchizawa
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2020
  • 卷号:181
  • 页码:1-13
  • DOI:10.4230/LIPIcs.ISAAC.2020.54
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要: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.
  • 关键词:Circuit complexity; neural networks; threshold circuits; sprase activity; tradeoffs
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