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Fault-Tolerant Deep Learning: A Hierarchical Perspective
- Publication Year :
- 2022
-
Abstract
- With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. In this context, reliability turns out to be critical to the deployment of deep learning in these applications and gradually becomes a first-class citizen among the major design metrics like performance and energy efficiency. Nevertheless, the back-box deep learning models combined with the diverse underlying hardware faults make resilient deep learning extremely challenging. In this special session, we conduct a comprehensive survey of fault-tolerant deep learning design approaches with a hierarchical perspective and investigate these approaches from model layer, architecture layer, circuit layer, and cross layer respectively.<br />Comment: Special session submitted to VTS'22
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2204.01942
- Document Type :
- Working Paper