Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. However, such systems need to support not only the high-performance capabilities at tight power/energy envelop, but also need to be intelligent/cognitive, self-learning, and robust. Hence, machine learning has rapidly proliferated into these systems. However, machine learning itself is inherently vulnerable to various reliability and security threats, which cannot be addressed by traditional measures. This talk will provides an overview of reliability and security challenges for a machine learning based system, followed by several analysis and mitigation techniques, investigated at my group, for realizing a robust machine learning system.

Muhammad Shafique is a full professor of Computer Architecture and Robust Energy-Efficient Technologies (CARE-Tech.) at the Embedded Computing Systems Group, Institute of Computer Engineering, Faculty of Informatics, Vienna University of Technology (TU Wien) since Nov. 2016. He received his Ph.D. in Computer Science from Karlsruhe Institute of Technology (KIT), Germany in Jan.2011. Before, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems for several years. He possesses an in-depth understanding of various video coding standards (HEVC, H.264, MVC, MPEG-1/2/4). His research interests are in computer architecture, power- & energy-efficient systems, robust computing, dependability & fault-tolerance, hardware security, emerging computing trends like Neuromorphic and Approximate Computing, neurosciences, emerging technologies & nanosystems, self-learning & intelligent systems, FPGAs, MPSoCs, and embedded systems. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems covering various layers of the hardware and software stacks (like micro-architecture, architecture, and run-time system software). The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. Dr. Shafique has given several Invited Talks, Tutorials, and Keynotes. He has also organized many special sessions at premier venues (like DAC, ICCAD, DATE, and ESWeek) and served as the Guest Editor for IEEE Design and Test Magazine (D&T) and IEEE Transactions on Sustainable Computing (T-SUSC). He has served as the TPC co-Chair of ESTIMedia and LPDC, General Chair of ESTIMedia, and Track Chair at DATE and FDL. He has served on the program committees of numerous prestigious IEEE/ACM conferences including ICCAD, ISCA, DATE, CASES, ASPDAC, and FPL. He is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a member of the ACM, SIGARCH, SIGDA, and SIGBED. He holds one US patent and has (co-)authored 4 Books, 4 Book Chapters, and over 180 papers in premier journals and conferences. Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences like CODES+ISSS, DATE, DAC and ICCAD, Best Master Thesis Award, DAC'14 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, and Best Lecturer Award at KIT.