IEEE Circuits and Systems Magazine - Q2 2021 - 69

and thus any ASIC-based accelerators will be months or
even years behind the state-of-the-art algorithms; on
the other hand, FPGAs can be dynamically updated as
needed. Second, robotic workloads are highly diverse,
thus it is difficult for any ASIC-based robotic computing
accelerator to reach economies of scale in the near
future; on the other hand, FPGAs are a cost-effective
and energy-effective alternative before one type of accelerator
reaches economies of scale. Third, compared
to SoCs that have reached economies of scale, e.g.,
mobile SoCs, FPGAs deliver a significant performance
advantage. Fourth, partial reconfiguration allows multiple
robotic workloads to time-share an FPGA, thus allowing
one chip to serve multiple applications, leading
to overall cost and energy reduction.
However, FPGAs are still not the mainstream computing
substrate for robotic workloads,
for several
reasons: first, FPGA programming is still much more
challenging than regular software programming, and
the supply of FPGA engineers is still limited. Second,
although there is significant progress in the past few
years in the FPGA High-Level Synthesis (HLS) automation,
such as [206], HLS is still not able to produce
optimized code, and IP supports for robotic workloads
are still extremely limited. Third, commercial
software support for robotic workloads on FPGAs
is still missing. For instance, there is no official ROS
support on any commercial FPGA platform today. For
robotic companies to fully exploit the power of FPGAs,
these problems need to be first addressed, and
the authors use these problems to motivate our future
research work.
Zishen Wan (Student Member, IEEE) is
currently a Ph.D. student in Electrical
and Computer Engineering at Georgia
Institute of Technology, Atlanta, GA,
U.S.A. He received the M.S. degree from
Harvard University, Cambridge, MA, in
2020, and the B.S. degree from Harbin Institute of Technology,
Harbin, China, in 2018, both in Electrical Engineering.
He has a broad research interest in VLSI design,
computer architecture, and edge intelligence, with a focus
on energy-efficient and robust hardware and system
design for autonomous machines. He has received the
Best Paper Award in DAC 2020 and CAL 2020.
Bo Yu (Senior Member, IEEE) received
the B.S. degree in electronic technology
and science from Tianjin University,
Tianjin, China, in 2006, and the Ph.D. degree
from the Institute of Microelectronics,
Tsinghua University, Beijing,
SECOND QUARTER 2021
China, in 2013. He is currently the CTO of PerceptIn, Fremont,
CA, U.S.A., a company focusing on providing visual
perception solutions for robotics and autonomous
driving. His current research interests include algorithm
and systems for robotics and autonomous vehicles. Dr.
Yu is also a Founding Member of the IEEE Special Technical
Community on Autonomous Driving.
Thomas Yuang Li (Student Member, IEEE)
is a research intern at PerceptIn, U.S.A.
and a student member of the IEEE. His research
interests include building autonomous
space explorers for future commercial
robotic space-exploration missions
as well as space robotics and computing related topics.
Jie Tang (Senior Member, IEEE) is currently
an associate professor in School
of Computer Science and Engineering
of South China University of Technology,
Guangzhou, China. She received
her B.E. degree From University of Defense
Technology and Ph.D. degree from the Beijing Institute
of Technology, both in Computer Science. She
was previously a visiting researcher at the Embedded
Systems Center at University of California, Irvine, USA,
and a research scientist at Intel China Runtime Technology
Lab. Dr. Tang is mainly doing research on Computing
Systems for Autonomous Machines. She is a
founding member and secretary of the IEEE Computer
Society Special Technical Community on Autonomous
Driving Technologies.
Yuhao Zhu (Member, IEEE) is an Assistant
Professor of Computer Science at
University of Rochester, U.S.A. His research
group focuses on applications
and computer systems for visual computing.
His work is recognized by the
Honorable Mention of the 2018 ACM SIGARCH/IEEE-CS
TCCA Outstanding Dissertation Award and multiple
IEEE Micro Top Picks designations. He is a recipient
of the NSF CAREER Award in 2020.
Yu Wang (Senior Member, IEEE) recei -
ved his B.S. degree in 2002 and Ph.D.
degree (with honor) in 2007 from Tsinghua
University, Beijing, China. He is currently
a Tenured Professor and Chair
with the Department of Electronic Engineering,
Tsinghua University. His research interests include
application specific hardware computing, parallel
circuit analysis, and power/reliability aware system
IEEE CIRCUITS AND SYSTEMS MAGAZINE
69
IEEE Circuits and Systems Magazine - Q2 2021

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q2 2021
