A Low-Energy Implementation of Finite Automata by Optimal-Size Neural Nets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00392404" target="_blank" >RIV/67985807:_____/13:00392404 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-40728-4_15" target="_blank" >http://dx.doi.org/10.1007/978-3-642-40728-4_15</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-40728-4_15" target="_blank" >10.1007/978-3-642-40728-4_15</a>
Alternative languages
Result language
angličtina
Original language name
A Low-Energy Implementation of Finite Automata by Optimal-Size Neural Nets
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. We investigate the energy complexity for recurrent networks which bounds the number of active neurons at any time instant of a computation. We prove that any deterministic finite automaton with m states can be simulated by a neural network of optimal size s=Theta(sqrt{m}) with time overhead O(s/e) per one input bit, using the energy O(e), for any e=Omega(log s) and e=O(s), which shows the time-energy tradeoff in recurrent networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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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
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning - ICANN 2013
ISBN
978-3-642-40727-7
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
114-121
Publisher name
Springer
Place of publication
Berlin
Event location
Sofia
Event date
Sep 10, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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