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About the Authors
Naveen Verma (nverma@princeton
.edu) received his B.A.Sc. degree from
the University of British Columbia,
Vancouver, Canada, in 2003 and his
M.Sc. and Ph.D. degrees from the
Massachusetts Institute of Technology, Cambridge, in 2005 and 2009,
respectively. He is a professor in the
Department of Electrical Engineering
at Princeton University, New Jersey,
where his research focuses on systems for intelligent sensing, including
sensors based on large-area electronics, algorithms for machine perception and control employing machine
learning, and heterogeneous computing platforms exploiting in-sensor
and in-memory computing. He is a
Member of the IEEE.
Hongyang Jia (hjia@princeton
.edu) received his B.Eng. degree in
microelectronics from Tsinghua University, Beijing, in 2014, and his M.A.
degree in electrical engineering from
Princeton University, New Jersey, in
2016, where he is currently pursuing
his Ph.D. degree. He is with the Department of Electrical Engineering at
Princeton, where his research focuses
on ultralow-energy system design for

inference applications. His research
interests include programmable inmemory computing platforms and
CMOS IC design leveraging approximate computing. He received the
Analog Devices Outstanding Student Designer Award in 2017. He is a
Student Member of the IEEE.
Hossein Valavi (hvalavi@princeton
.edu) received his B.Sc. degree in electrical engineering from the Sharif University of Technology, Tehran, Iran, in
2013 and his M.A. degree in electrical
engineering from Princeton University,
New Jersey, in 2015, where he is currently pursuing his Ph.D. degree. His
research focuses on ultralow-energy
system design for signal processing
and machine-learning applications.
He was a recipient of the Analog Devices Outstanding Student Designer
Award in 2016. He is a Student Member of the IEEE.
Yinqi Tang (yinqit@princeton.edu)
received his B.S. degree in microelectronics from Fudan University,
Shanghai, China, in 2014 and his M.A.
degree in electrical engineering from
Princeton University, New Jersey, in
2016, where he is currently pursuing
his Ph.D. degree in the Department of
Electrical Engineering. His research
interests include energy-efficient
ha rdw a re systems for machinelearning and deep-learning applications, focusing on both algorithmic
and hardware design. He is a Student
Member of the IEEE.
Murat Ozatay (mozatay@princeton
.edu) received his B.Sc. degree in electrical and electronics engineering
from the Middle East Technical University, Ankara, Turkey, in 2015 and
his M.A. degree in electrical engineering from Princeton University, New
Jersey, in 2017, where he is currently pursuing his Ph.D. degree in the
Department of Electrical Engineering. His research focuses on bringing
together algorithms and insights for
learning with technologies and systems for advanced sensing. His other
research interests include machine
learning, artificial intelligence, the
Internet of Things, and the design of

very-large-scale integration systems.
He is a Student Member of the IEEE.
Lung-Yen Chen (lungyenc@princeton
.edu) received his B.S. degree in electrical engineering from National Taiwan University, Taipei, in 2011 and
his M.A. degree in electrical engineering from Princeton University, New
Jersey, in 2014, where he is currently
pursuing his Ph.D. degree in the
Department of Electrical Engineering.
His research interests include digital
architectures and circuit design for inmemory computing and multimedia
applications, with emerging technologies such as magnetic random-access
memory and 3D integration. He is a
Student Member of the IEEE.
Bonan Zhang (bonanz@princeton
.edu) received his B.Eng. degree in
electrical and computer engineering
from McGill University, Montréal, in
2017 and his M.A. degree in electrical
engineering from Princeton University, New Jersey, in 2019, where he is
currently pursuing his Ph.D. degree in
the Department of Electrical Engineering. His research interests include
IC design for in-memory computing
architecture with emerging technologies, such as magnetic random-access
memory, and exploring algorithmic
approaches for hardware relaxation
to enable machine-learning applications. He received the Analog Devices
Outstanding Student Designer Award
in 2019. He is a Student Member of
the IEEE.
Peter Deaville (deaville@princeton
.edu) received his B.S. degree in electrical engineering from the University of Maryland, College Park, in
2018. He joined Princeton University,
New Jersey, in the spring of 2019 as
a Ph.D. student in the Department
of Electrical Engineering, where his
research focuses on exploring inmemory computing using magnetic
random-access memory technology.
His other research interests include
circuit design for machine learning,
leveraging emerging methods and
technologies. He is a Student Member
of the IEEE.

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