Autonomous Vehicle Engineering - April 2022 - 10

Artificial Intelligence/Machine Learning
Teaching or training a deep neural network is accomplished by feeding
the deep neural network data, allowing the DNN to make a prediction as
to what the data represents.
based on its training. These data-intensive compute
operations require specialized solutions. Systems must
feature large amounts of high-speed, solid-state data
storage. They also must be hardened for deployment in
vehicles that are constantly moving, subject to violent
shock and vibration and other harsh environmental
factors. Ideal design pairs the software-based functions
of deep learning with ruggedized hardware strategies
optimized for both edge and cloud processing.
Deep-learning training, explained
Although the most challenging and time-consuming
method of creating AI, deep-learning training gives a
deep neural network (DNN) its ability to accomplish
a task. DNNs, comprised of many layers of interconnected
artificial neurons, must learn to perform a
particular AI task, such as translating speech to text,
image classification, video cataloging, or generating a
recommendation system. This is achieved by feeding
data to the DNN, which it then uses to predict what
the data signifies.
For instance, a DNN might be taught how to
Deep learning inference is performed by feeding new data, such as new
images, to the network, giving the DNN a chance to classify the image.
grounded in software algorithms and fueled by deeplearning
training and deep-learning inference models
that are essential to faultless performance.
Enabling these vital and instantaneous processes
requires AI algorithms to be trained and then deployed
on-vehicle. It's a process that has developers tapping
into both sophisticated software design and smart
hardware strategies to protect vehicle performance
that could be a matter of life or death.
While deep-learning training and deep-learning
inference may sound like interchangeable terms, each
has a very different role to play in systems that keep
drivers safe and distinguish OEMs with increasingly
intelligent auto features. Deep-learning training employs
datasets to teach a deep neural network to complete an
AI task, like image or voice recognition. Deep-learning
inference is the process of feeding the same network
with novel or new data, to predict what that data means
differentiate three different objects - a dog, a car,
and a bicycle. The first step puts together a data set
consisting of thousands of images that include dogs,
cars, and bikes. The second step feeds the images to
the DNN and empowers it to ascertain what the image
represents. When an inaccurate prediction is made, the
artificial neurons are revised, correcting the error so
future inferences are more accurate. In this process, it
is likely that the network will better predict the image's
true nature each consecutive time it is presented.
The training process continues until the DNN's
predictions meet the desired level of accuracy. At this
point, the trained model is sufficiently prepared to use
new images to make predictions.
Deep-learning training can be extremely
compute-intensive, with billions upon billions of
calculations often necessary for training a DNN. The
method relies on robust computing power to run
calculations quickly. Performed in data centers, deep
neural-network training leverages multi-core processors,
GPUs, VPUs and other performance accelerators
to advance AI workloads with enormous speed
and accuracy.
10 April 2022
AUTONOMOUS VEHICLE ENGINEERING
Autonomous Vehicle Engineering - April 2022

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