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CESG Seminar: “What Really Lies beyond Moore’s Law?”

CESG Seminar: “What Really Lies beyond Moore’s Law?”

October 6 @ 4:10 pm - 5:10 pm

Abstract: In the past decades, Moor’s Law has become almost the synonym of the rapid pace of performance improvements in computing.  Fifty years’ continuous scaling has had tremendous impact on not only many fields in engineering and technology, but also numerous aspects of modern society. As the scaling is getting close to the physics limits, nowadays it has become trendy to declare that Moore’s Law is dead. However there are common misconceptions on what Moore’s Law really is, and more importantly, on why Moore’s Law has been so powerful. In this talk, I will provide explanations to clear those misconceptions. I will also discuss future research directions as computing goes through this inflection point. Bio: Dr. Frank Liu is a Research Staff Member at IBM Research. He is also an adjunct faculty in the ECE Department of Texas A&M University. He has broad research activities in VLSI design, VLSI technology-design co-optimization, design for manufacturing, design automation, circuit simulation, cyber-physical system modeling, as well as bioinformatics and high-performance computing. He is a Fellow of IEEE.

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CESG Seminar: “Embracing Sparsity in Deep Networks: From Algorithms to Hardware”

CESG Seminar: “Embracing Sparsity in Deep Networks: From Algorithms to Hardware”

October 13 @ 4:10 pm - 5:10 pm

Abstract: In this talk, I will present how the sparsity, a desirable property for both algorithms and hardware design, could be discovered, exploited, and enhanced in the context of deep networks. I will first introduce how an iterative sparse solver could be linked to and tuned as a feed-forward deep network, using the “unfolding then truncating” trick. Next, I will show how a double sparse dictionary structure could be naturally utilized to sparsify the weights of the obtained networks resulting in a new deep network whose feature and parameter spaces are simultaneously sparsified. I will then describe PredictiveNet, a recent work by one of my collaborators, which predicts the zero hidden activations of the nonlinear CNN layers at low costs thereby skipping a large fraction of convolutions in CNNs at runtime without modifying the CNN structure or requiring additional branch networks. Such an energy-efficient hardware implementation could be seamlessly integrated with the theory bridging sparse optimization and deep learning, potentially leading to even larger energy savings. Bio: Dr. Zhangyang (Atlas) Wang is an Assistant Professor of Computer Science and Engineering (CSE), at the Texas A&M University (TAMU), since August 2017. He is currently leading a research group of 5 Ph.D. students and 3 M.S. students and has several projects supported by DARPA, Adobe, etc. During 2012-2016, he was a Ph.D. student in the Electrical and Computer Engineering (ECE) Department, at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang. Prior to that, he obtained the B.E. degree at the University of Science and Technology of China (USTC) in 2012. Dr. Wang’s research has been addressing machine learning, computer vision and multimedia signal processing problems using advanced feature learning and optimization techniques. He has co-authored 40 papers, and published several books and chapters. He has been granted 3 patents, and has received over 15 research awards and scholarships. His research has been covered by worldwide media such as BBC, Fortune, International Business Times, UIUC news and alumni magazine. More can be found at: http://www.atlaswang.com.

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CESG FISHBOWL SEMINAR: “Integrating robots in cognitive solutions: application studies and system architecture implications”

CESG FISHBOWL SEMINAR: “Integrating robots in cognitive solutions: application studies and system architecture implications”

October 19 @ 2:30 pm - 3:30 pm

Title: “Integrating robots in cognitive solutions: application studies and system architecture implications” Abstract: As cognitive systems have become increasingly capable, cloud-based services have enabled the integration of conversational robotic agents and human agents into into cognitive solutions. These robots are sophisticated mobile sensor platforms and compelling dialog agents. Cognitive solutions involving robots offer a rich domain for computing systems research. This talk will focus on research using conversational robotic agents for wellness and tracking in an aging-in-place lab and on individualized robotic dialog systems. System architectures, data services, and memory and compute optimizations to support the robotic agents will be presented.   Biography: Dr. Kevin Nowka is the Director of IBM Research – Austin, one of IBM’s 12 global research laboratories. He leads a team of scientists and engineers working on optimized systems for big-data and analytics, cognitive computing systems, cloud infrastructure, and energy-efficient systems and datacenters. He is also IBM Senior State Executive for Texas responsible for government, community, and university relations in Texas. He received a B.S. degree in Computer Engineering from Iowa State University, Ames, in 1986 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1988 and 1995, respectively. He has 79 US issued patents and has published over 70 technical papers on circuits, systems and processor design, and technology issues. He is an IBM Master Inventor and a member of the IBM Academy of Technology. Dr. Nowka is also an adjunct professor at Texas A&M University in the Electrical and Computer Engineering Department, is a member of the Texas Science and Engineering Fair Advisory Board, serves as Vice Chair of the Grace Academy Board of Trustees, and is a member of the TEES Advisory Board.

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CESG Seminar: “Model Checking Asynchronous Systems: A New Technique and a Practical Tool”

CESG Seminar: “Model Checking Asynchronous Systems: A New Technique and a Practical Tool”

October 20 @ 4:10 pm - 5:10 pm

Dr. Jeff Huang, Assistant Professor , Computer Science & Engineering, Texas A & M University Abstract:  Model checking is a state-of-the-art (and probably the most practical) approach for formal verification of safety- and security-critical systems. Unfortunately, despite more than three decades of research, it remains challenging to model check real-world asynchronous systems due to the infamous state-explosion problem caused by system-level asynchrony. In this talk, I will describe a recent advance in model checking, namely “maximal causality reduction”, developed by my group over the last three years. By reducing the state space exploration to embarrassingly-parallel constraint satisfaction problems and by eliminating redundant state space explorations, maximal causality reduction makes model checking dramatically more scalable than existing techniques such as bounded model checking and partial order reduction. We have developed a practical model checking tool based on this technique, and have applied it on a variety of asynchronous systems including real-world web servers and browsers and found tens of serious bugs in these systems. Recently, we have also released our tool as open source and publicly available on Github. Biography: Dr. Jeff Huang an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on developing techniques and tools for improving performance, safety and security of complex software systems based on fundamental program analyses and programming language theory. His research has won awards including ACM SIGPLAN PLDI Distinguished Paper Award, SIGPLAN Research Highlights, ACM SIGSOFT Outstanding Dissertation Award, Google Faculty Research Award, and NSF CAREER Award.  

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CESG FISHBOWL TELESEMINAR: “Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices”

CESG FISHBOWL TELESEMINAR: “Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices”

October 26 @ 2:30 pm

Title: “Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices” Abstract: Today’s high-resolution VR apps can only run on special VR hardware gears (tethered or the upcoming 60Ghz-based wireless). As a result, the VR industry has been stagnating due to a painful “chicken-and-egg” dilemma: there is a lack of VR content/apps due to limited VR hardware gears sold (about one million units), but VR hardware sale is stagnating because of the lack of VR content/apps in the market.We envision the most promising way to break away from this dilemma is to enable high-resolution VR apps to run on commodity mobile devices such as the billion smartphones. To realize the vision, we have embarked on a course of R&D that aims to: (1) understand the fundamental technological (hardware) challenges in solving the problem (today and in the near future), and (2) explore software innovations to circumvent the hardware constraints. In preliminary work, we have developed a new VR rendering architecture that supports single-player high-resolution VR apps on Google Pixel XL, over 802.11ac, with under 14ms latency and 60 FPS.   Biography: Dr. Y. Charlie Hu is a Professor of Electrical and Computer Engineering and Computer Science (by courtesy), and an ACM Distinguished Scientist and IEEE Fellow. He received his Ph.D. in Computer Science from Harvard in 1997. He is a recipient of the Honda Initiation Grant Award, NSF CAREER Award, Purdue University Faculty Scholar, Purdue CoE Early Career Research Award, and EuroSys Best Student Paper Award. His research interests include mobile systems, distributed systems, and computer networks. He served as a general co-chair of ACM SIGCOMM 2014, and PC co-chair of ACM MobiCom 2016 and ACM SIGOPS EuroSys 2018. Since 2010, his group has conducted pioneering work on energy profiling and energy debugging on smartphones which has been widely covered by news media such as ABC News, NBC News, BBC, Times of India, MIT Tech Review, and Scientific American. The technology has been commercialized and is extending the battery life of hundreds of millions of smartphones.

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CESG Seminar: “Knowledge-driven representations of physiological signals: Developing measurable indices of non-observable behavior”

CESG Seminar: “Knowledge-driven representations of physiological signals: Developing measurable indices of non-observable behavior”

October 27 @ 4:10 pm - 5:10 pm

Dr. Theodora Chaspari, Assistant Professor, Computer Science & Engineering, Texas A & M University Abstract:  Recent converging advances in sensing and computing, including wearable technologies, allow the unobtrusive long-term tracking of individuals yielding rich multimodal signal measurements from real-life. In this thesis, we will present the development of data-scientific and context-rich bio-behavioral approaches for analyzing, quantifying, and interpreting these bio-behavioral signals. We propose a novel knowledge-driven signal representation framework able to efficiently handle the large volume of acquired data and the noisy signal measurements. Our approach involves the use of sparse approximation techniques and the design of signal-specific dictionaries learned through Bayesian methods, outperforming previously proposed models in terms of signal reconstruction and information retrieval criteria. We further focus on translating the derived signal representations into novel intuitive quantitative measures analyzed with probabilistic and statistical models in relation to external factors of observable behavior. This work has found applications in Autism intervention for detecting beneficial regulation mechanisms during child-therapist interactions, as well as in the family studies domain for identifying instances of emotional escalation and interpersonal conflict. These are discussed in relation to designing human-assistive personalized bio-feedback systems able to promote healthy routines, increase emotional wellness and awareness, and empower clinical assessment and intervention. Biography: Dr. Theodora Chaspari is an Assistant Professor at the Computer Science & Engineering Department in Texas A&M University. She has received her diploma (2010) in Electrical and Computer Engineering from the National Technical University of Athens, Greece and a Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010 and 2017, she had been working as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (summer 2015). Dr Chaspari’s research interests lie in the areas of biomedical signal processing, human-computer interaction, behavioral signal processing, data science, and machine learning. She is a recipient of the USC Annenberg Graduate Fellowship, USC Women in Science and Engineering Merit Fellowship, and the IEEE Signal Processing Society Travel Grant.

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