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Energy-Efficient Interfaces for World-to-Cloud Computing
Monday, February 15, 2016 - 3:00pm to 4:00pm
Refreshments and discussion following
Presenter: Vanessa Chen
Abstract: Our world is rapidly moving towards a wireless future where everything will stay connected largely through the internet, and we have come to rely on smart devices for sensing, communication, and computing.
This presentation will demonstrate how rethinking the fundamental nature of circuits and systems leads to dramatically more energy efficient and more robust solutions. To reduce power consumption for ubiquitous sensing, machine learning is applied to the system-driven design for bio-information acquisition. Only the critical information is acquired in the sensor array, and the system could identify the bio-information correctly with more than 10 times power reduction in the interface circuits. The other approach to achieve better energy efficiency is to enhance the design with algorithms. A time- interleaved analog-to-digital converter, which needs a large comparator array to work in parallel to resolve data at 20GS/s, will be discussed. A low-frequency clock is used to synchronize the high-throughput data, and the randomness of process mismatch is exploited to compensate for the clock misalignment. The algorithm breaks the tradeoff between speed and power, and allows the system to be calibrated in the background as a self-healing system. This work achieves the best reported power efficiency for the gigahertz analog-to-digital converters.
Vanessa Chen received her Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, in 2013. From 2010 to 2013, at Carnegie Mellon, she focused her research on self-healing systems and high-speed ADC designs, and held a research internship position at IBM T. J. Watson Research Center, Yorktown Heights, in 2012. Her work has appeared in top academic conferences and has been featured as highlights in the IEEE International Solid-State Circuit Conference (ISSCC). She was the recipient of the IBM Ph.D. Fellowship in 2012 and Analog Devices Outstanding Student Designer Award in 2013. Prior to that, she received her B.S. and M.S. degrees in Electrical Engineering from National Tsing Hua University, Taiwan, in 2003 and 2005, respectively. In 2005, she joined Realtek Semiconductor working on analog front-end circuits of xDSL chipsets. She is currently with Qualcomm working on low-power data- acquisition systems for mobile devices. Her research interests focuses on energy-efficient analog-digital interface designs for ubiquitous sensing and communication systems.