Energy Complexity of Fully-Connected Layers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00573359" target="_blank" >RIV/67985807:_____/23:00573359 - isvavai.cz</a>
Result on the web
<a href="https://dx.doi.org/10.1007/978-3-031-43085-5_1" target="_blank" >https://dx.doi.org/10.1007/978-3-031-43085-5_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-43085-5_1" target="_blank" >10.1007/978-3-031-43085-5_1</a>
Alternative languages
Result language
angličtina
Original language name
Energy Complexity of Fully-Connected Layers
Original language description
The energy efficiency of processing convolutional neural networks (CNNs) is crucial for their deployment on low-power mobile devices. In our previous work, a simplified theoretical hardware-independent model of energy complexity for CNNs has been introduced. This model has been experimentally shown to asymptotically fit the power consumption estimates of CNN hardware implementations on different platforms. Here, we pursue the study of this model from a theoretically perspective in the context of fully-connected layers. We present two dataflows and compute their associated energy costs to obtain upper bounds on the optimal energy. Using the weak duality theorem, we further prove a matching lower bound when the buffer memory is divided into two fixed parts for inputs and outputs. The optimal energy complexity for fully-connected layers in the case of partitioned buffer ensues. These results are intended to be generalized to the case of convolutional layers.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Computational Intelligence. IWANN 2023 Proceedings, Part I
ISBN
978-3-031-43084-8
ISSN
0302-9743
e-ISSN
—
Number of pages
13
Pages from-to
3-15
Publisher name
Springer
Place of publication
Cham
Event location
Ponta Delgada
Event date
Jun 19, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001155313400001