PHYSIOLOGICAL-READOUT

TUTORIALS
Sunday February 5 th
Long Yan received the B.S. and M.S. degree in electrical engineering from the Korea Advanced
Institute of Science and Technology (KAIST), Daejeon, Korea, in 2007 and 2009, respectively,
and the Ph.D. degree from the same university in 2011.
In 2010, he was with the Microsystems Technology Laboratories at the Massachusetts
Institute of Technology (MIT), Cambridge, MA, USA, as a visiting student, where he developed
a low-power EEG readout front-end circuit for patient-specific seizure classification. From
2011 to 2014, he has been with IMEC, Leuven, Belgium, as a Senior Scientist. His research
at IMEC has focused on the development of low-power mixed-mode circuits for wearable and
implantable applications. Since December 2014, he joined Samsung Electronics, Hwaseong,
South Korea where he is now leading analog interface circuit developments for bio-sensors.
From 2015, Dr. Yan serves on the technical program committee of the International Solid
State Circuit Conference (ISSCC).
10:30 AM
T4 Energy-Efficient Processors for Deep Learning
Marian Verhelst, KU Leuven, Heverlee, Belgium
Deep learning has become popular for image recognition, and more recently, for other pattern
matching tasks (e.g., speech processing, text analysis, etc.). Deep learning, however, is
associated with significant computational complexity, which until recently, made it feasible
only on power-hungry server platforms. However, we now see a trend towards embedded
processing of deep-learning networks.
After an introduction into deep learning and its implementation challenges, this tutorial will
give an overview of processing architectures to enable efficient network evaluations on
embedded platforms. This discussion is tightly interwoven with coverage of emerging
implementation-driven algorithmic innovations, to understand the impact of new deep learning
algorithms on embedded hardware design. The tutorial will give the audience an
understanding of the opportunities and implementation challenges in embedded deep
learning, and enable one to comprehend research on deep learning processors.
Marian Verhelst is assistant professor at MICAS – KU Leuven, Belgium. Her research focuses
on embedded machine learning, low-power sensing and processing for the internet-of-things.
Marian is member of the Young Academy of Belgium, the ISSCC and DATE executive
committees and is an associate editor of JSSC.
10:30 AM
T5 Fundamentals of Time-Based Circuits
Matt Straayer, Maxim Integrated Products, North Chelmsford, MA
Time-based circuits have recently been developed as an alternative and enhancement to
voltage-based signal processing due to the availability of advanced process nodes with fast
transistors and low supply voltages. These time-based circuits offer unique attributes, ranging
from simple, area-efficient quantizers to more complex techniques for time-based processing
such as interpolation and noise shaping. This tutorial will cover the fundamentals and metrics
of time-based circuits, highlight a number of architectures that directly utilize time, and
discuss tradeoffs for these circuits in the context of various application requirements,
including a comparison to more traditional voltage-based circuits.
Matt Straayer received the Ph.D. degree from MIT in 2008. He worked at Integrated Sensing
Systems, Inc. and MIT Lincoln Laboratory, before he helped to co-found Cambridge Analog
Technologies, Inc. (CAT), Billerica, MA, where he led the technical development of zerocrossing
circuits. In 2011 CAT was acquired by Maxim Integrated, where he has continued to
advance mixed-signal circuit architectures and develop enabling IP for a wide range of product
areas and applications.
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