Talks and Poster Presentations (with Proceedings-Entry):

M. Hanif, F. Khalid, R. Putra, S. Rehman, M. Shafique:
"Robust Machine Learning Systems: Reliability and Security for Deep Neural Networks";
Talk: 24th IEEE International Symposium on On-Line Testing And Robust System Design (IOLTS'18), Platja d'Aro, Spain; 2018-07-02 - 2018-07-04; in: "2018 IEEE 24th International Symposium on On-Line Testing and Robust System Design (IOLTS)", (2018), ISBN: 978-1-5386-5992-2; 257 - 260.

English abstract:
Machine learning is commonly being used in almost all the areas that involve advanced data analytics and intelligent control. From applications like Natural Language Processing (NLP) to autonomous driving are based upon machine learning algorithms. An increasing trend is observed in the use of Deep Neural Networks (DNNs) for such applications. While the slight inaccuracy in applications like NLP does not have any severe consequences, it is not the same for other safety-critical applications, like autonomous driving and smart healthcare, where a small error can lead to catastrophic effects. Apart from high-accuracy DNN algorithms, there is a significant need for robust machine learning systems and hardware architectures that can generate reliable and trustworthy results in the presence of hardware-level faults while also preserving security and privacy. This paper provides an overview of the challenges being faced in ensuring reliable and secure execution of DNNs. To address the challenges, we present several techniques for analyzing and mitigating the reliability and security threats in machine learning systems.

Machine Learning, Deep Neural Networks, DNNs, Reliability, Security, Soft Errors, Aging, Process Variations, Hardware

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.