Autonomous Vehicle Engineering - April 2022 - 13

Artificial Intelligence/Machine Learning
associated with processing deeplearning
inference algorithms via the
cloud, coupled with unique high performance.
For example, GPUs and TPUs
accelerate the ability to perform myriad
linear algebra computations, enabling
the system to parallelize such operations.
Rather than the CPU performing
AI inference computations, the GPU
or TPU - better at performing math
computations - tackles the workload,
significantly accelerating inference
analysis while the CPU focuses on
running the rest of the applications and
the operating system.
Local inference processing also
eliminates latency problems and solves
internet bandwidth issues related to
raw data transmission, particularly
large video feeds. Multiple wired and
wireless connectivity technologies,
such as Gigabit Ethernet, 10 Gigabit
Ethernet, Wi-Fi 6, and Cellular 4G LTE
allow the system to maintain internet
connection in a range of situations.
Up-and-coming 5G wireless connectivity
expands options even further with
its significantly faster data rate, much
lower latency, and improved bandwidth.
These rich connectivity options enable
the offload of mission-critical data to the
cloud and accommodate over-the-air
updates. In addition, CANBus support
empowers the solution to log vehicle
data from vehicle buses and networks.
Vehicle speed, wheel speed, engine rpm,
steering angle and other rich data can
be assessed for real-time insight and
important information about the vehicle.
Big data, big opportunity
To bring an ever-growing list of automated-driving
capabilities to market,
ADAS developers have been focused on
improving the algorithms that impact
features and performance. But specialized
hardware is necessary - AI edge-inference
computers are the hardened
computing solutions developed for this
process, built to withstand exposure
to dust, debris, shock, vibration and
extreme temperatures and designed to
collect process, and store a vast amount
of data from multiple sources.
Amassing data is only step one to
fueling ADAS; software development
and specialized hardware strategies
must work together for smarter, safer,
and more highly-automated vehicles. ■
Dustin Seetoo is director of product marketing
at Premio Inc., a global solutions provider
specializing in computing technology for
enterprises with complex, highly specialized
requirements. dustin.seetoo@premioinc.com
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Autonomous Vehicle Engineering - April 2022

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Autonomous Vehicle Engineering - April 2022 - CVR4
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