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 language | English (US) |
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Title of host publication | Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing |
Subtitle of host publication | Software Optimizations and Hardware/Software Codesign |
Publisher | Springer Nature |
Pages | 303-328 |
Number of pages | 26 |
ISBN (Electronic) | 9783031399329 |
ISBN (Print) | 9783031399312 |
DOIs | |
State | Published - 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