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Figure 1:
HSA provides a unified view
of fundamental computing elements,
allowing a programmer to write
applications that seamlessly integrate
CPUs with GPUs while benefiting from
the best attributes of each.
Figure 2:
Unibap’s mission-critical
stereo Intelligent Vision System
(IVS) with 70 mm baseline features
advanced heterogeneous processing.
Extensive error correction is enabled
on the electronics and particularly on
the integrated AMD G-Series SoC and
Microsemi SmartFusion2 FPGA.
sensors and the compute units. These
systems not only provide high speed
and high resolution to compete with
our human vision, they also provide
accurate spatial information on where
landmarks or objects are located. To
achieve this, stereoscopic vision is the
natural choice. Industrial applications
for this type of stereoscopic vision
system can be found, for example,
in item-picking from unsorted bins.
Mounted on a robot arm, a vision
system can carry out ‘visual servoing’
with 50 fps and identify the most
suitable item to pick at the same
time the gripper of the robot arm
is approaching the bin. This makes
scanning - which can take a couple
of seconds – and reprogramming the
robot arm superfluous. Autonomous
cars are another obvious application
for vision technologies, as well as
a whole range of domestic robot
applications.
The artificial visual cortex
So how does this process work in
detail? The first stages of information
handling are strictly localized to each
pixel, and are therefore executed
in a FPGA. Common to all machine
vision is the fact that color cameras
think in RGB (and the pixels are
Red, Green and Blue) just like the
human eye, but this method is not
suitable for accurately calculating an
image. Thus, firstly RGB has to be
transferred into HIS (Hue, Saturation
and Intensity). Rectifying the image
to compensate for distortion in the
lenses is the next necessary step.
Following this, stereo matching
can be performed between the two
cameras. These steps are executed
within an FPGA that is seconding the
x86 core processor. All the following
calculations are application-specific
and best executed on the integrated,
highly flexible programmable x86
processor platform which has to fulfill
quite challenging tasks to understand
and interpret the content of a picture.
To understand how complex
these tasks are, it is necessary to
understand that interpreting picture
content is extremely complex for
software programmers and that,
until recently, the human visual
cortex has been superior to computer
technology. These days, however,
technological advancements are,
quite literally, changing the game:
An excellent example of computer
technology improvement is Google’s
AlphaGo computer which managed to
beat the world’s best Go player. And
this was achieved by executing neural
network algorithms. Is this really so
revolutionary? Haven’t we seen neural
network algorithms in the recent past?
Indeed we have. Neural networks are
not new. They are just one of many
AI (Artificial Intelligence) methods.
Although exactly this kind of network
was considered very promising in the
nineties it is even more promising
today as all the basic technologies
now have far more computing power.
Progress simply came to a halt due to
the limited performance. To be even
more precise, the barrier came down
partly due to a lack of compute power
and partly due to problems with
networks with too many hidden layers.
Recent methods use even more layers
in building the neural networks and
today the term deep-learning means a
neural network with many more layers
than were used previously. Plus, the
heterogeneous system architecture
of modern SoCs allows deep-learning
algorithms to be used efficiently (e.g.
with the Deep Learning Framework
Caffe from Berkley).
x86 technology is also interesting
for intelligent stereoscopic machine
vision systems due to its optimized
streaming and vector instructions
developed over a long period of
time and very extensive and mature
software ecosystem, vision system
algorithms and driver base. Plus, new
initiatives like Shared Virtual Memory
(SVM) and the Heterogeneous
System Architecture (HSA) now offer
an additional important companion
technology to x86 systems by
increasing the raw throughput
capacities needed for intelligent
machine vision.
HSA enables efficient use
of all resources
With the introduction of latest
generation AMD SoCs, a hardware
ecosystem is now in place which
accelerates artificial intelligence
58 l New-Tech Magazine Europe