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Wednesday, November 14, 2018 at 4:15pm
Title: Accelerated Machine Intelligence: An Edge to Cloud Continuum
ABSTRACT: This talk presents, Project PHI (Accelerated System Design for Pervasive Hierarchical Intelligence) a holistic effort to provide a comprehensive solution for making immersive machine intelligence a reality. Our guiding principle is to retain as much generality and automation while delivering large performance and efficiency gains through specialization and acceleration for a wide range of learning and intelligence workloads. As the first milestones of Project PHI, we have developed Tabla and DnnWeaver, which publicly available (http://act-lab.org/artifacts/tabla/ and http://dnnweaver.org/). DnnWeaver is the very first open-source hardware acceleration stack for deep neural networks. Tabla is a cross-stack solution—spanning from programming language to the hardware—that rethinks the hardware/software abstraction by delving into the theory of machine learning. It leverages the insight that many learning algorithms can be solved using stochastic gradient descent that minimizes an objective function. The solver is fixed while the objective function changes with the learning algorithm. Therefore, Tabla uses stochastic optimization as the abstraction between hardware and software. Consequently, programmers specify the learning algorithm by merely expressing the gradient of the objective function in our domain specific language. Tabla then automatically generates the synthesizable implementation of the accelerator and the system software for scale-out FPGA realization using a set of template designs. Real hardware measurements show orders of magnitude higher performance and power efficiency while the programmer only writes 60 lines of code. Next, the talk ventures to the edge domain and shows how utilizing algorithmic insights enables us to match the server-grade GPU performance for DNN acceleration within milli-Watt regime and extend the discussion to our very recent work on complete stack for motion planning and control in robotics, dubbed RoboX. The encouraging results from these full-stack solutions in Project PHI shows that rethinking the hardware/software abstractions from an algorithmic perspective can open new dimensions in system design for Pervasive Hierarchical Intelligence.
BIO: Dr. Esmaeilzadeh was awarded early tenure at the University of California, San Diego (UCSD), where he is the inaugural holder of Halicioglu Chair in Computer Architecture with the rank of associate professor in Computer Science and Engineering. Prior to UCSD, he was an assistant professor in the School of Computer Science at the Georgia Institute of Technology from 2013 to 2017. There, he was the inaugural holder of the Catherine M. and James E. Allchin Early Career Professorship. Hadi is the founding director of the Alternative Computing Technologies (ACT) Lab, where his team is developing new technologies and cross-stack solutions to build the next generation computer systems. He is also the associate director of Center for Machine Integrated Compu=ng and Security (MICS) at UCSD. Dr. Esmaeilzadeh obtained his Ph.D. from the Department of Computer Science and Engineering at the University of Washington in 2013 where his Ph.D. work received the 2013 William Chan Memorial Best Dissertation Award. Prof. Esmaeilzadeh received the IEEE Technical Committee on Computer Architecture (TCCA) “Young Architect” Award in 2018 and was inducted to the ISCA Hall of Fame in the same year. He has received the Air Force Office of Scientific Research Young Investigator Award (2017), College of Computing Outstanding Junior Faculty Research Award (2017), Qualcomm Research Award (2017 and 2016), Google Research Faculty Award (2016 and 2014), Microsoft Research Award (2017 and 2016), and Lockheed Inspirational Young Faculty Award (2016). His teams were awarded the Qualcomm Innovation Fellowship in 2014 and 2018, one of his students was a Microsoft Research Fellow, and another won the 2017 National Center for Women & IT (NCWIT) Collegiate Award. Four of his undergraduate students have been awarded the Georgia Tech President’s Undergraduate Research Award (PURA). His research has been recognized by four Communications of the ACM Research Highlights, four IEEE Micro Top Picks, a nomination for Communications of the ACM Research Highlights, an honorable mention in IEEE Micro Top Picks, and a Distinguished Paper Award in HPCA 2016. Hadi’s work on dark silicon has also been profiled in New York Times. More information is available on his webpage, http://cseweb.ucsd.edu/~hadi/.