Energy-Time Tradeoff in Recurrent Neural Nets

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00472477" target="_blank" >RIV/67985807:_____/15:00472477 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-09903-3_3" target="_blank" >http://dx.doi.org/10.1007/978-3-319-09903-3_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-09903-3_3" target="_blank" >10.1007/978-3-319-09903-3_3</a>

Result language
angličtina
Original language name
Energy-Time Tradeoff in Recurrent Neural Nets
Original language description
In this chapter, we deal with the energy complexity of perceptron networks which has been inspired by the fact that the activity of neurons in the brain is quite sparse (with only about 1% of neurons firing). This complexity measure has recently been introduced for feedforward architectures (i.e., threshold circuits). We shortly survey the tradeoff results which relate the energy to other complexity measures such as the size and depth of threshold circuits. We generalize the energy complexity for recurrent architectures which counts the number of simultaneously active neurons at any time instant of a computation. We present our energy-time tradeoff result for the recurrent neural nets which are known to be computationally as powerful as the finite automata. In particular, we show the main ideas of simulating any deterministic finite automaton by a low-energy optimal-size neural network. In addition, we present a lower bound on the energy of such a simulation (within a certain range of time overhead) which implies that the energy demands in a fixedsize network increase exponentially with the frequency of presenting the input bits.
Czech name
—
Czech description
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Project
<a href="/en/project/GBP202%2F12%2FG061" target="_blank" >GBP202/12/G061: Center of excellence - Institute for theoretical computer science (CE-ITI)</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Artificial Neural Networks. Methods and Applications in Bio-/Neuroinformatics
ISBN
978-3-319-09902-6
ISSN
2193-9349
e-ISSN
—
Number of pages
12
Pages from-to
51-62
Publisher name
Springer
Place of publication
Cham
Event location
Sofia
Event date
Sep 10, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000380528700003

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