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Energy Complexity of Recurrent Neural Networks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00393985" target="_blank" >RIV/67985807:_____/14:00393985 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1162/NECO_a_00579" target="_blank" >http://dx.doi.org/10.1162/NECO_a_00579</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1162/NECO_a_00579" target="_blank" >10.1162/NECO_a_00579</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Energy Complexity of Recurrent Neural Networks

  • Original language description

    Recently, a new so-called energy complexity measure has been introduced and studied for feedforward perceptron networks. This measure is inspired by the fact that biological neurons require more energy to transmit a spike than not to fire, and the activity of neurons in the brain is quite sparse, with only about 1% of neurons firing. In this paper, we investigate the energy complexity of recurrent networks which counts the number of active neurons at any time instant of a computation. We prove that anydeterministic finite automaton with m states can be simulated by a neural network of optimal size s=Theta(sqrt{m}) with the time overhead of tau=O(s/e) per one input bit, using the energy O(e), for any e such that e=Omega(log s) and e=O(s), which shows the time-energy tradeoff in recurrent networks. In addition, for the time overhead tau satisfying tau^tau=o(s), we obtain the lower bound of s^{c/tau} on the energy of such a simulation, for some constant c>0 and for infinitely ma

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GAP202%2F10%2F1333" target="_blank" >GAP202/10/1333: NoSCoM: Non-Standard Computational Models and Their Applications in Complexity, Linguistics, and Learning</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2014

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Neural Computation

  • ISSN

    0899-7667

  • e-ISSN

  • Volume of the periodical

    26

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    21

  • Pages from-to

    953-973

  • UT code for WoS article

    000334027800005

  • EID of the result in the Scopus database