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Events for June 23, 2017
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Software-Hardware Co-Design for Efficient Neural Network Acceleration on FPGA
Fri, Jun 23, 2017 @ 10:30 AM - 11:30 PM
Thomas Lord Department of Computer Science, Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yu Wang, Tsinghua University
Talk Title: Software-Hardware Co-Design for Efficient Neural Network Acceleration on FPGA
Abstract: Artificial neural networks, efficiency compared with general-purpose processors. However, the long development period and insufficient performance of traiditional FPGA acceleration prevent it from wide utilization. We propose a complete design flow to achieve both fast deployment and high energy efficiency for accelerating neural networks on FPGA [FPGA 16, FPGA 17 best paper]. Deep compression and data quantization are employed to exploit the redundancy in algorithm and reduce both computational and memory complexity. Two architecture designs for CNN and DNN/RNN are proposed together with compilation environment. Evaluated on Xilinx Zynq 7000 and Kintex Ultrascale series FPGA with real-world neural networks, up to 15 times higher energy efficiency can be achieved compared with mobile GPU and desktop GPU. Finally, we will discuss the possibilities and trends of adopting emerging NVM technology for efficient learning systems to further improve the energy efficiency.
Biography: Yu Wang is currently a tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. He received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He has published over 150 papers in refereed journals and conferences in Design Automation and FPGA related area. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology.
Host: Viktor Prasanna, prasanna@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Kathy Kassar
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AI Seminar
Fri, Jun 23, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Kiri Wagstaff, JPL
Talk Title: Mining Mars Targets from the Planetary Science Literature
Abstract: Every day, rovers on Mars send back data for new observation targets (e.g., rocks, soils, layers). Some of these targets yield new discoveries that are published in the scientific literature. Yet there is currently no accessible link between data (or targets) and their subsequent publications. We are building the Mars Target Encyclopedia (MTE) to enable users to ask questions such as "What do we know about target Epworth?" and "What are all of the Mars targets that contain chlorine?" We use information extraction and machine learning methods to mine the steadily growing body of scientific publications and extract compositional knowledge about Mars surface targets. The MTE benefits Mars mission planners, planetary scientists, and the interested public by condensing relevant knowledge into a central resource in an accessible way. More than just a literature search, the MTE allows us to ask new questions that previously could not be answered.
Biography: Dr. Kiri L. Wagstaff is a principal researcher in artificial intelligence and machine learning and a tactical uplink lead for the Mars rover Opportunity at the Jet Propulsion Laboratory. Her research focuses on developing new machine learning and data analysis methods, particularly those that can be used for in situ analysis onboard spacecraft such as orbiters, landers, and rovers. She holds a Ph.D. in Computer Science from Cornell University followed by an M.S. in Geological Sciences from the University of Southern California and an MLIS from San Jose State University. She received a 2008 Lew Allen Award for Excellence in Research for work on the sensitivity of machine learning methods to high-radiation space environments and a 2012 NASA Exceptional Technology Achievement award for work on transient detection methods in radio astronomy data. She is passionate about keeping machine learning relevant to real-world problems.
Host: Mayank Kejriwal
Webcast: http://webcastermshd.isi.edu/Mediasite/Play/1b4d78b79cb3438d8b67b5129a5b3f3b1dLocation: Information Science Institute (ISI) - 11th floor large conference room
WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/1b4d78b79cb3438d8b67b5129a5b3f3b1d
Audiences: Everyone Is Invited
Contact: Kary LAU
