We held our next tinyML Talks webcast. Muhammad Shafique from New York University Abu Dhabi presented Energy-Efficiency and Security for TinyML and EdgeAI: A Cross-Layer Approach on February 1, 2022.
Modern Machine Learning (ML) approaches like Deep Neural Networks (DNNs) have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require huge processing, memory, and energy costs, besides being vulnerable to several security threats. This talk will present challenges and cross-layer frameworks for building highly energy-efficient and robust machine learning systems for the tinyML and EdgeAI applications, which jointly leverage optimizations at different software and hardware layers, e.g., neural accelerator, memory access optimizations, approximations, hardware-aware NAS and network compression. These cross-layer techniques enable new opportunities for improving the area, power/energy, and performance efficiency of systems by orders of magnitude, which is a crucial step towards enabling the wide-scale deployment of resource-constrained embedded AI systems like UAVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, etc.
Dr. Shafique received his Ph.D. degree from the Karlsruhe Institute of Technology (Germany) in 2011. In Oct.2016, he joined the Institute of Computer Engineering at Technische Universität Wien (Vienna, Austria) as a Full Professor. Since Sep.2020, he is with the Division of Engineering, New York University Abu Dhabi (NYU-AD, UAE), and is a Global Network faculty at the NYU Tandon School of Engineering (USA). His research interests are in design automation and system level design for brain-inspired computing, AI & machine learning hardware, neuromorphic computing, approximate computing, wearable healthcare devices and systems, autonomous systems, energy-efficient systems, robust computing, hardware security, emerging technologies, FPGAs, MPSoCs, and embedded systems. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains.
Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials, as well as organized many special sessions at premier venues. He has served as the PC Chair, General Chair, Track Chair, and PC member for several prestigious IEEE/ACM conferences. Dr. Shafique holds one U.S. patent has (co-)authored 6 Books, 10+ Book Chapters, 300+ papers in premier journals and conferences, and 50+ archive articles. He received the 2015 ACM/SIGDA Outstanding New Faculty Award, AI 2000 Chip Technology Most Influential Scholar Award in 2020, six gold medals, and several best paper awards and nominations at prestigious conferences. He is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC.
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Muhammad Shafique
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Muhammad Shafique
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