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Events for April 21, 2014
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New Solutions for High-Level Synthesis
Mon, Apr 21, 2014 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Deming Chen, University of Illinois at Urbana-Champaign
Talk Title: New Solutions for High-Level Synthesis
Abstract: While technology scaling has presented many new and exciting opportunities, new design challenges have arisen due to increased density and stringent delay and power constraints for VLSI circuits. A significant problem is the growing gap between rapidly increasing silicon capacity and the design productivity that lags behind. High-level synthesis (HLS) has been touted as a solution to this problem, as it can significantly reduce the number of man hours required for a design by raising the level of design abstraction. However, existing HLS solutions have several limitations, and studies show that the design quality of HLS can be noticeably worse than that of manual RTL design. In this talk, I will present several new techniques we developed to drastically improve HLS solutions. These include design entry with parallel languages, smart design space exploration, logic/high-level co-optimization, automatic iterative improvement, and communication optimization across multiple modules, etc. Meanwhile, powerful source-to-source compilation and polyhedral model-based code analysis and transformation are used to enable effective realization of some of these techniques. For example, our code transformation and optimization using polyhedral model can enable data streaming across two communicating hardware modules through HLS, which achieved 30X execution speedup over the baseline on average. At the end of the talk, I will also briefly introduce other on-going research activities in my lab in the areas of GPU computing, nanotechnology, and computational genomics.
Biography: Deming Chen is an Associate Professor of Electrical and Computer Engineering of University of Illinois at Urbana-Champaign. He received his Ph.D. in Computer Science from University of California at Los Angeles in 2005. His research interests include system- and high-level synthesis, compilation and programming for heterogeneous platforms, nano-systems design, GPU optimization, and bioinformatics. He is a technical committee member for a series of conferences, including FPGA, ASPDAC, ICCD, ISQED, DAC, ICCAD, DATE, ISLPED, FPL, etc. He is or has been an associated editor for TCAD, TVLSI, TODAES, TCAS-I, JCSC, and JOLPE. He is the program chair and general chair for several conferences. He received five Best Paper Awards, the NSF CAREER Award in 2008, the ACM SIGDA Outstanding New Faculty Award in 2010, and IBM Faculty Award in 2014. He is a senior member of IEEE. He was involved in two startup companies. He implemented his published algorithm on CPLD technology mapping when he was a software engineer in Aplus Design Technologies, Inc. in 2001, and the software was exclusively licensed by Altera. He is one of the inventors of the xPilot high-level synthesis package developed at UCLA, which was licensed to AutoESL Design Technologies, Inc.
Host: Massoud Pedram
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Seminars in Biomedical Engineering
Mon, Apr 21, 2014 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Bo Han, Ph.D., Assistant Professor of Research Departments of Surgery and Biomedical Engineering, University of Southern California
Talk Title: Microenvironment in Tissue Engineering
Host: David D'Argenio
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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A Computational Framework for Diversity in Ensembles of Humans and Machine Systems
Mon, Apr 21, 2014 @ 03:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kartik Audhkhasi, University of Southern California
Talk Title: A Computational Framework for Diversity in Ensembles of Humans and Machine Systems
Abstract: My Ph.D. thesis presents a computational framework for diversity in ensembles or collections of humans and machine systems used for signal and information processing. Machine system ensembles have out-performed single systems across many pattern recognition tasks ranging from automatic speech recognition to online recommendation. Likewise, ensembles are central to computing with humans, for example, in crowd sourcing-based data tagging and annotation in human behavioral signal processing. This widespread use of ensembles, albeit largely heuristic, is motivated by their robustness to the ambiguity in production, representation, and processing of real-world information. Diversity or complementarity of the individual humans and machine systems is widely-accepted as a key ingredient in ensemble design. I will present a computational framework for this diversity by addressing three important problems - modeling, analysis, and design.
I will first propose the Globally-Variant Locally-Constant (GVLC) model for the labeling behavior of a diverse ensemble. The GVLC model captures the data-dependent reliability and diverse behavior of an ensemble through a latent state-dependent noisy channel. I will next present the Generalized Ambiguity Decomposition (GAD) theorem that defines ensemble diversity for a broad class of statistical learning loss functions and relates this diversity to ensemble performance. I will show an application of the GAD theorem by theoretically and empirically linking the diversity of an automatic speech recognition system ensemble with the word error rate of the fused hypothesis. The final part of my thesis will present techniques to design a diverse ensemble of machine systems, ranging from maximum entropy models to sequence classifiers. I will also prove that introducing diversity in the training data through careful noise addition speeds-up the maximum likelihood training of Restricted Boltzmann Machines and feed-forward neural networks.
Biography: Kartik Audhkhasi received the B.Tech. degree in Electrical Engineering and the M.Tech. degree in Information and Communication Technology from the Indian Institute of Technology, Delhi. He is currently an Electrical Engineering Ph.D. candidate at the University of Southern California (USC), Los Angeles. His research focuses on a computational framework for diversity in ensembles of humans and machine systems for signal and information processing. He is broadly interested in statistical signal processing, speech processing and recognition, machine learning, and human-centered computing.
He is a recipient of the Annenberg fellowship, the IBM Ph.D. fellowship, and was a 2012 USC Ming Hsieh Institute Ph.D. Scholar. Kartik was part of the USC team that won the Interspeech-2013 Computational Paralinguistics Challenge. He has also received best paper and best teaching assistant awards from the Electrical Engineering Department at USC.
Host: Prof. Shrikanth S. Narayanan
Location: Ronald Tutor Hall of Engineering (RTH) - 320
Audiences: Everyone Is Invited
Contact: Talyia Veal
