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Events for September 29, 2017
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Ming Hsieh Institute Seminar Series on Integrated Systems
Fri, Sep 29, 2017 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Bodhisatwa Sadhu, Research Staff Member, IBM T.J. Watson Research Center
Talk Title: mmWave Radio Design for 5G Base-stations and Mobile Handsets
Host: Profs. Hossein Hashemi, Mike Chen, Mahta Moghaddam, and Dina El-Damak
More Information: MHI Seminar Series IS -Bodhisatwa Sadhu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Jenny Lin
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Munushian Seminar - Ming C. Wu, Friday, September 22nd at 2:00pm in EEB 132
Fri, Sep 29, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ming C. Wu, University of California, Berkeley
Talk Title: Silicon Photonic MEMS
Abstract: Ming C. Wu is Nortel Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is also Co-Director of Berkeley Sensor and Actuator Center (BSAC) and Faculty Director of UC Berkeley Marvell Nanolab. Dr. Wu received his M.S. and Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 1988. He has been with AT&T Bell Laboratories, Murray Hill (1988-1992) and UCLA (1993 to 2004) before joining the faculty at Berkeley. His research interests include optoelectronics, nanophotonics, MEMS, and optofluidics. He has published 8 book chapters, over 500 papers in journals and conferences, and 25 issued U.S. patents.
Prof. Wu is an IEEE Fellow, and a Packard Foundation Fellow (1992 - 1997). He received the 2007 Paul F. Forman Engineering Excellence Award, the 2017 C.E.K. Mees Medal from Optical Society of America, and the 2016 William Streifer Award from IEEE Photonics Society.
Biography: Ming C. Wu is Nortel Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is also Co-Director of Berkeley Sensor and Actuator Center (BSAC) and Faculty Director of UC Berkeley Marvell Nanolab. Dr. Wu received his M.S. and Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 1988. He has been with AT&T Bell Laboratories, Murray Hill (1988-1992) and UCLA (1993 to 2004) before joining the faculty at Berkeley. His research interests include optoelectronics, nanophotonics, MEMS, and optofluidics. He has published 8 book chapters, over 500 papers in journals and conferences, and 25 issued U.S. patents.
Prof. Wu is an IEEE Fellow, and a Packard Foundation Fellow (1992 - 1997). He received the 2007 Paul F. Forman Engineering Excellence Award, the 2017 C.E.K. Mees Medal from Optical Society of America, and the 2016 William Streifer Award from IEEE Photonics Society.
Host: EE-Electrophysics
More Info: minghsiehee.usc.edu/about/lectures
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: minghsiehee.usc.edu/about/lectures
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Powering the Future of Imaging and Signal Processing with Data-Driven Systems
Fri, Sep 29, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Saiprasad Ravishankar, Electrical Engineering & Computer Science Department, University of Michigan
Talk Title: Powering the Future of Imaging and Signal Processing with Data-Driven Systems
Series: Medical Imaging Seminar Series
Abstract: The data-driven learning of signal models including dictionaries, sparsifying transforms, low-rank models, tensor and manifold models, etc., is of great interest in many applications. In this talk, I will present my research that developed efficient, scalable, and effective data-driven models and methodologies for signal processing and imaging. I will mainly discuss my work on transform learning. Various interesting structures for sparsifying transforms such as well-conditioning, double sparsity, union-of-transforms, incoherence, rotation invariance, etc., can be considered, which enable their efficient and effective learning and usage. Transform learning-driven approaches achieve promising results in applications such as image and video denoising, and X-ray computed tomography or magnetic resonance image (MRI) reconstruction from limited or corrupted data. The convergence properties of the algorithms will be discussed. I will also present recent work on efficient dictionary learning in combination with low-rank models, and demonstrate the usefulness of the resulting LASSI method for dynamic MRI. The efficiency and effectiveness of the methods proposed in my research may benefit a wide range of additional applications in imaging, computer vision, neuroscience, and other areas requiring data-driven parsimonious models. Finally, I will provide a brief overview of recent works on physics-driven deep training of image reconstruction algorithms, light field reconstruction from focal stacks, online data-driven estimation of dynamic data from streaming, limited measurements, etc.
Biography: Saiprasad Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, in 2008. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 2010 and 2014 respectively, from the University of Illinois at Urbana-Champaign, where he was an Adjunct Lecturer in the Department of Electrical and Computer Engineering during Spring 2015, and a Postdoctoral Research Associate at the Coordinated Science Laboratory until August, 2015. Since then, he has been a Research Fellow in the Electrical Engineering and Computer Science Department at the University of Michigan. His research interests include signal, image and video processing, signal modeling, data science, dictionary learning, biomedical and computational imaging, data-driven methods, inverse problems, compressed sensing, machine learning, and large-scale data processing.He has received multiple awards including the Sri Ramasarma V Kolluri Memorial Prize from IIT Madras and the IEEE Signal Processing Society Young Author Best Paper Award for his paper Learning Sparsifying Transforms published in IEEE Transactions on Signal Processing.
Host: Professor Richard Leahy
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Talyia White
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Fri, Sep 29, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yanzhi Wang , Syracuse University
Talk Title: Towards the limits of energy efficiency and performance of deep learning systems
Abstract: Deep learning systems have achieved unprecedented progresses in a number of fields such as computer vision, robotics, game playing, unmanned driving and aerial systems, and other AI-related fields. However, the rapidly expanding model size is posing a significant restriction on both the computation and weight storage, for both inference and training, and on both high-performance computing systems and low-power embedded system and IoT applications. In order to overcome these limitations, we propose a holistic framework of incorporating structured matrices into deep learning systems, and could achieve (i) simultaneous reduction on weight storage and computational complexities, (ii) simultaneous speedup of training and inference, and (iii) generality and fundamentality that can be adopted to both software and hardware implementations, different platforms, and different neural network types, sizes, and scalability.
Besides algorithm-level achievements, our framework has (i) a solid theoretical foundation to prove that our approach will converge to the same "effectiveness" as deep learning without compression, and to demonstrate/prove that our approach approach/achieve the theoretical limitation of computation and storage of deep learning systems; (ii) platform-specific implementations and optimizations on smartphones, FPGAs, and ASIC circuits. We demonstrate that our smartphone-based implementation achieves the similar speed of GPU and existing ASIC implementations on the same application. Our FPGA-based implementations for deep learning systems and LSTM networks could achieve 11X+ energy efficiency improvement compared with the best state-of-the-arts, and even higher energy efficiency gain compared with IBM TrueNorth neurosynaptic processor. Our proposed framework can achieve 3.5 TOPS computation performance in FPGAs, and is the first to enable nano-second level recognition speed for image recognition tasks.
Biography: Yanzhi Wang is currently an assistant professor in the Department of Electrical Engineering and Computer Science at Syracuse University, from August 2015. He has received his Ph.D. Degree in Computer Engineering from University of Southern California (USC) in 2014, under supervision of Prof. Massoud Pedram, and his B.S. Degree in Electronic Engineering from Tsinghua University in 2009.
Dr. Wang's current research interests are the energy-efficient and high-performance implementations of deep learning and artificial intelligence systems, neuromorphic computing and new computing paradigms, and emerging deep learning algorithms/systems such as Bayesian neural networks, generative adversarial networks (GANs), and deep reinforcement learning. Besides, he works on the application of deep learning and machine intelligence in various mobile and IoT systems, medical systems, and UAVs, as well as the integration of security protection in deep learning systems. He also works on near-threshold computing for IoT devices and energy-efficient cyber-physical systems. His group works on both algorithms and actual implementations (FPGAs, circuit tapeouts, mobile and embedded systems, and UAVs).
His work has been published in top venues in conferences and journals (e.g. ASPLOS, MICRO, ICML, DAC, ICCAD, DATE, ASP-DAC, ISLPED, INFOCOM, ICDCS, TComputer, TCAD, etc.), and has been cited for around 3,000 times according to Google Scholar. He has received four Best Paper or Top Paper Awards from major conferences including IEEE ICASSP (top 3 among all 2,000+ submissions), ISLPED, IEEE CLOUD, and ISVLSI. He has another six Best Paper Nominations and two Popular Papers in IEEE TCAD. His group is sponsored by the NSF, DARPA, IARPA, AFRL/AFOSR, Syracuse CASE Center, and industry sources.
Host: Paul Bogdan
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Estela Lopez
