Hardware and Software Optimizations for Capsule Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU154886" target="_blank" >RIV/00216305:26230/23:PU154886 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-39932-9_12" target="_blank" >http://dx.doi.org/10.1007/978-3-031-39932-9_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-39932-9_12" target="_blank" >10.1007/978-3-031-39932-9_12</a>
Alternative languages
Result language
angličtina
Original language name
Hardware and Software Optimizations for Capsule Networks
Original language description
Among advanced Deep Neural Network models, Capsule Networks (CapsNets) have shown high learning and generalization capabilities for advanced tasks. Their capability to learn hierarchical information of features makes them appealing in many applications. However, their compute-intensive nature poses several challenges for their deployment on resource-constrained devices. This chapter provides an optimization flow at the software and at the hardware level for improving the energy efficiency of the CapsNets' execution.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
ISBN
978-3-031-39932-9
Number of pages of the result
26
Pages from-to
303-328
Number of pages of the book
477
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
UT code for WoS chapter
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