Muhammad Shafique

Title Enabling Extreme Energy-Efficiency through Brain-Inspired Computing Trends: From Approximate to Neural Processing
Abstract

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT) / Internet of Everything (IoE), and 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. Therefore, 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. The basic problems of sharp increase in power densities in on-chip systems according to the discontinuation of Dennard Scaling force to rethink. As the process technology shrinks and the per-transistor performance/power efficiency is not keeping pace with the well-known power-reduction techniques (like DVFS and power-gating) at various abstraction layers, continuing to support precise computing across the stack is most likely not sufficient to solve the rising energy-efficiency challenges. Approximate Computing (aka InExact Computing) relies on relaxing the bounds of precise/exact computing to provide new opportunities for improving the area, power/energy, and performance efficiency of systems by orders of magnitude at the cost of reduced output quality. This talk will provide an introduction to the emerging trend of approximate computing followed by our cross-layer approximate computing framework that covers various abstraction layers of the hardware/software stacks, i.e. ranging from the circuit layer to all the way up to the application layer. This talk provides a systematical understanding of how to generate and explore the design space of approximate components (adders and multipliers) and accelerators, as well as our corresponding open-source libraries, which enable a wide-range of power/energy, performance, area and output quality tradeoffs, and a high degree of design flexibility to facilitate their design. Towards the end, this talk will discuss challenges and opportunities for building energy-efficient and adaptive architectures and hardware accelerators for machine learning, and how approximate computing can play an important role.

Bio

Muhammad Shafique is a full-professor of Computer Architecture and Robust Energy-Efficient Technologies (CARE-Tech.) at the Institute of Computer Engineering, Vienna University of Technology (TU Wien), Austria. His research interests are in robust and energy-efficient systems, computer architecture, emerging brain-inspired computing trends like neuromorphic and approximate computing, neurosciences, emerging technologies and nanosystems, hardware security, adaptive and self-learning, wearable healthcare devices, FPGAs, MPSoCs, and embedded systems. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing systems covering various layers of the hardware and software stacks (like micro-architecture, architecture, and run-time systems). 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 been a PI in several projects, and led teams within projects of multi-million Euros research funding. Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier forums. He has also organized many special sessions at top-tier conferences (like DAC, ICCAD, and DATE) and has served as the Guest Editor for IEEE Design and Test Magazine and IEEE Transactions on Sustainable Computing. He has served as the General Chair and TPC Chair of ESTIMedia, TPC co-Chair of LPDC, and Track Chair at DATE. He is/was 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, SIGBED, and HiPEAC. He holds one US patent and has (co-)authored 4 Books, 3 Book Chapters, and over 180 papers in premier journals and conferences. Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award (given world-wide to one person per year) for demonstrating an outstanding potential as a lead researcher and educator in the field of electronic design automation [http://www.sigda.org/onfa]. His other achievements include six gold medals in his educational career, and several Best Paper Awards and Nominations at top-tier conferences (like DAC, ICCAD, DATE, and CODES+ISSS), 30 HiPEAC Paper Awards, DAC 2014 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, Best Master Thesis Award, and Best Lecturer Award.