Autonomous Vehicle Engineering - April 2022 - 12

Artificial Intelligence/Machine Learning
With configurable processing power, edge inference computers (Premio AI shown) are capable of running machine learning
and deep learning inference analysis at the edge. Performance accelerators including multi-core processors, GPUs, VPUs,
TPUs, FPGAs, and NVMe computational storage devices increase capability, especially in modern computer architecture
designs. Equipped with a rich I/O, autonomous vehicle data recorders offer ample USB Type-A ports (Gen 3.2 10Gbps),
RJ45 and M12 Gigabit Ethernet ports, RJ45 and M12 PoE+ ports, Serial COM ports, and others, enabling connection to the
cameras and sensors to data computers.
Deep-learning inference
An extension of deep-learning training, deep-learning
inference uses a fully trained DNN to make predictions
based on new, never-seen-before data closer to where
its generated. By feeding new data, such as images, to
the network, deep learning inference enables DNN
classification of the image. For example, adding to the
'dog, car, bicycle' example, new images of these and
other objects can be loaded into the DNN allowing
image classification. The fully trained DNN now can
accurately predict the image's identity.
Once a DNN is fully trained, it can be copied
to other devices. DNNs can be extremely large,
containing hundreds of layers of artificial neurons
and connecting billions of weights. Before it can be
deployed, the network must be modified to require
less computing power, energy and memory. The result
is a slightly less accurate model, but this is offset by its
simplification benefits.
Two methods can be deployed to modify
the DNN; pruning or quantization. In pruning, a
data scientist feeds data to the DNN and observes.
Non-firing or rarely firing neurons are identified and
removed without causing significant reduction in
prediction accuracy. Quantization involves reducing
weight precision. For example, a 32-bit floating-point
reduced to an 8-bit floating-point creates a small model
that consumes fewer compute resources. Both methods
have negligible impact on model accuracy. At the same
time, the models become much smaller and faster,
resulting in less energy use and lower consumption
of compute resources.
Making the edge work in ADAS
Deep-learning inference 'at the edge' has commonly
used a hybrid model in which an edge computer
harvests information from a sensor or camera and
transmits that information to the cloud. However,
latency occurs as data often requires a few seconds
to be delivered to the cloud, analyzed, and returned
- unacceptable for applications requiring real-time
inference analysis or detection. An AV moving at 60
mph (96 km/h) could travel more than 100 feet (30
m) without guidance in just a few seconds.
In contrast, purpose-built edge computing devices
perform inference analysis in real time for split-second
autonomous decision-making. These industrial-grade
AI inference computers are designed to endure challenging
in-vehicle deployments. Tolerant to a variety
of power-input scenarios, including being powered by
a vehicle battery, systems are ruggedized for expected
exposure to impact, vibration, extreme temperature,
dust and other environmental challenges.
These characteristics alleviate many of the issues
12 April 2022
AUTONOMOUS VEHICLE ENGINEERING

Autonomous Vehicle Engineering - April 2022

Table of Contents for the Digital Edition of Autonomous Vehicle Engineering - April 2022