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February 2018

CESG SEMINAR: “Energy-efficient Machine Learning Systems for Cloud and Edge Computing”

February 23 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Dr. Yingyan Lin, TI Research Assistant Professor at Rice University “Energy-efficient Machine Learning Systems for Cloud and Edge Computing” Abstract: Machine learning (ML) algorithms are increasingly pervasive in tackling the data deluge of the 21st Century. Current ML systems adopt either a centralized cloud computing or a distributed edge computing paradigm. In both paradigms, the challenge of energy efficiency has been drawing increased attention. In cloud computing, data transfer due to inter-chip, inter-board, inter-shelf and inter-rack communications (I/O interface) within data centers is one of the dominant energy costs. This will only intensify with the growing demand for increased I/O bandwidth for high-performance computing in data centers. On the other hand, in edge computing, energy efficiency is the primary design challenge, as edge devices have limited energy, computation and storage resources. This challenge is being exacerbated by the need to embed ML algorithms, such as convolutional neural networks (CNNs), for enabling local on-device inference capabilities. In this talk, I will present holistic system-to-circuit approaches for addressing these energy efficiency challenges. First, I will describe the design of a 4 GS/s bit-error-rate optimal analog-to-digital converter in 90nm CMOS and its use in realizing an energy-efficient 4 Gb/s serial link receiver for I/O interface. Measurement results have shown that this technique provides a promising solution to the well-known interface power bottleneck problem in data centers. Next, I will describe two techniques that can potentially enable on-device deployment of CNNs by significantly reducing the energy consumption via algorithmic/architectural innovation. Finally, I will present some of our on-going research projects in the emerging areas of machine learning on resource-constrained mobile platforms.   Biography: Yingyan Lin is currently a TI research assistant professor in the Department of Electrical and Computer Engineering at Rice University. She received the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2017. Prior to that, she worked at China’s National Research Center for Integrated Circuits in Wuhan where she designed three analog and mixed-signal circuit IPs that were acquired by TOSHIBA Microelectronics Corporation in Japan. Her research interests include analog and mixed-signal circuits, error resiliency techniques, and VLSI circuits and architectures for machine learning systems on resource-constrained platforms. She was the recipient of the 2nd place Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016) and the 2016 Robert T. Chien Memorial Award at UIUC for Excellence in Research.

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