Information Processing in Silicon

Past few decades have seen unprecedented growth in the information processing capabilities of electronic systems such as desktops, laptops, mobiles phone etc. This emergence of advanced data processing systems has revolutionized several industries and has led to the availability of vast amounts of data. Recent advances in machine learning and big data explore ways of deriving useful conclusions from the available data but at a significant cost in silicon. Hence, it has now become crucial to ask, “what is the best way to build information processing systems for the future?”. This session invites researchers working on addressing various aspects of this question, including but not limited to, advances in state-of-the-art digital and analog CMOS-based designs, advances in state-of-the-art computer architectures and compilers, ways of addressing challenges such as high device variability and leakage power, alternative computing paradigms such as bio-neuro-inspired computing or computing using beyond-CMOS devices, alternative storage paradigms such as in-memory computers, novel memories such as RRAM or MRAM etc.

Confirmed Speakers

Keynote Speaker

Professor Vivienne Sze, MIT

Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department.  Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for multimedia applications such as computer vision, autonomous navigation, machine learning and video compression. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she developed algorithms and hardware for the latest video coding standard H.265/HEVC.  She is a co-editor of the book entitled “High Efficiency Video Coding (HEVC): Algorithms and Architectures” (Springer, 2014). 

Dr. Sze received the B.A.Sc. degree from the University of Toronto in 2004, and the S.M. and Ph.D. degree from MIT in 2006 and 2010, respectively. In 2011, she was awarded the Jin-Au Kong Outstanding Doctoral Thesis Prize in electrical engineering at MIT for her thesis on “Parallel Algorithms and Architectures for Low Power Video Decoding”.  She is a recipient of the 2017 Qualcomm Faculty Award, 2016 Google Faculty Research Award, 2016 AFOSR Young Investigator Award, 2016 3M Non-tenured Faculty Award, 2014 DARPA Young Faculty Award, 2007 DAC/ISSCC Student Design Contest Award and a co-recipient of the 2016 MICRO Top Picks Award and 2008 A-SSCC Outstanding Design Award.

More information about our research in the Energy-Efficient Multimedia Systems group can be found at: http://www.rle.mit.edu/eems/