Hardware and Software Optimizations for Capsule Networks

Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Vojtech Mrazek, Muhammad Abdullah Hanif, Maurizio Martina, Guido Masera, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
Subtitle of host publicationSoftware Optimizations and Hardware/Software Codesign
PublisherSpringer Nature
Pages303-328
Number of pages26
ISBN (Electronic)9783031399329
ISBN (Print)9783031399312
DOIs
StatePublished - Jan 1 2023

Keywords

  • Capsule networks
  • Deep learning
  • Energy efficiency
  • Hardware accelerator
  • Software optimizations

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering
  • General Social Sciences

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