@inproceedings{843bb605bcbb44bc98cca474091fbebf,
title = "Cross-layer approaches for improving the dependability of deep learning systems",
abstract = "Deep Neural Networks (DNNs) - the state-of-the-art computational models for many Artificial Intelligence (AI) applications - are inherently compute and resource-intensive and, hence, cannot exploit traditional redundancy-based fault mitigation techniques for enhancing the dependability of DNN-based systems. Therefore, there is a dire need to search for alternate methods that can improve their reliability without high expenditure of resources by exploiting the intrinsic characteristics of these networks. In this paper, we present cross-layer approaches that, based on the intrinsic characteristics of DNNs, employ software and hardware-level modifications for improving the resilience of DNN-based systems to hardware-level faults, e.g., soft errors and permanent faults.",
keywords = "cross-layer, deep learning, dependability, DNNs, faults, hardware accelerators, neural networks, reliability, soft errors, systems",
author = "Hanif, {Muhammad Abdullah} and Hoang, {Le Ha} and Muhammad Shafique",
note = "Publisher Copyright: {\textcopyright} 2020 ACM. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020 ; Conference date: 25-05-2020 Through 26-05-2020",
year = "2020",
month = may,
day = "25",
doi = "10.1145/3378678.3391884",
language = "English (US)",
series = "Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020",
publisher = "Association for Computing Machinery, Inc",
pages = "78--81",
editor = "Sander Stuijk",
booktitle = "Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2020",
}