IEEE Solid-State Circuits Magazine - Fall 2017 - 138

cars use many high-resolution image
sensors, relying on circuit advances
to maximize throughput and decrease
power consumption. Of course, the
extensive data generated by the
multitude of sensors require a very
powerful compute system. Graphics
processing units, field programmable
gate arrays, and custom application
specified integrated circuits are all
used, balancing tradeoffs in hardware acceleration. The self-driving
car also has unique memory system
requirements, and very high-performance DRAM is needed in terms of
bandwidth, but this must be balanced
with the need for low power and high
levels of integration.
The same topic was covered in one
of the Circuits short courses, "Integrated Circuits for Smart Connected Cars
and Automated Driving," which featured
seven distinguished presenters covering a wide range of topics. The session began with an excellent overview
on automotive electronics, drawing
on the real-world experience of Bosch.
Technical talks on previously mentioned sensor modalities were included,
such as Prof. Kawahito from Shizuoka
University covering advances in image
sensors to deliver state-of-the-art
dynamic range and low-noise perfor-

mance. Other types of sensors, including
inertial microelectromechanical systems (MEMS) and anisotropic magnet resistance angle sensors, were
covered as well. Connectivity was an
important topic, with Marvell covering
in-car networks to support high-speed
data interface and Intel discussing a
variety of topics that included 5G and
secure wireless platforms for intervehicle communication.
During one of the technical sessions,
Denso Corporation, in collaboration
with Toyota R&D, presented a single
photon avalanche detector (SPAD)
array chip targeted for automotive
lidar in Figure 3. The paper described
the world's first two-dimensional pixel
array with a SPAD macro pixel architecture directly outputting digital
data. Time-domain statistical processing is used to improve the accuracy of
time-of-flight distance measurement. The
chip is built into an imaging system
with a laser and scanning MEMS mirror, and this system is capable of imaging targets at ranges up to 20 m, even in
full 75-klux ambient light.

Deep Learning in Circuits
Machine learning is one of the highly
anticipated enabling technologies for
self-driving cars. In the automotive

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application and others, learning in circuits was another focus area for the
symposium, beginning with a short
course, "Machine Learning for Circuit
Designers." The short course covered
the basics of machine learning as well
as state-of-the-art implementation
techniques from a range of associated applications. For example, Prof.
Verma of Princeton University described
energy-efficient mixed-signal systems
for machine-learning algorithms. In
addition, a talk from Fujitsu focused on
large-scale neural networks utilized in
the cloud, and Qualcomm reviewed
machine-learning architectures for
mobile devices. Topics were selected to
be of interest to the circuit-level engineer, including parallelization, accelerators, the importance of data movement,
and memory management.
Advanced computing architectures,
including those specific to neuromorphic devices, were covered in another
joint focus session. Investigators from
IBM Research in Japan discussed neuromorphic device architectures with
support for new computing systems
that can learn from unstructured data.
Both static random-access memory
(SRAM) based spiking neural networks
and cross-bar structures were explored
as well as advances in solder bumping

SAC305
Cu Pilar

50 µm

FIGURE 4: Micro-bumping images with pitches down to 40 μm*, from IBM Research's neuromorphic computing paper.

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IEEE SOLID-STATE CIRCUITS MAGAZINE

Table of Contents for the Digital Edition of IEEE Solid-State Circuits Magazine - Fall 2017