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MARCHISIO, A.; BUSSOLINO, B.; COLUCCI, A.; MRÁZEK, V.; HANIF, M.; MARTINA, M.; MASERA, G.; SHAFIQUE, M.
Original Title
Hardware and Software Optimizations for Capsule Networks
English Title
Type
Chapter in a book
Original Abstract
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.
English abstract
Keywords
capsule networks, hardware, software, neural architecture search
Key words in English
Authors
RIV year
2025
Released
01.01.2023
Publisher
Springer Nature Switzerland AG
Location
Cham
ISBN
978-3-031-39932-9
Book
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
Pages from
303
Pages to
328
Pages count
26
BibTex
@inbook{BUT193587, author="MARCHISIO, A. and BUSSOLINO, B. and COLUCCI, A. and MRÁZEK, V. and HANIF, M. and MARTINA, M. and MASERA, G. and SHAFIQUE, M.", title="Hardware and Software Optimizations for Capsule Networks", booktitle="Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing", year="2023", publisher="Springer Nature Switzerland AG", address="Cham", pages="303--328", doi="10.1007/978-3-031-39932-9\{_}12", isbn="978-3-031-39932-9" }