February 2018
6
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over
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tory
The Rise of Edge Computing
By Mark Patrick,
Mouser Electronics
Edge computing in IoT is opening
new opportunities for embedded
designers. FPGAs can be used to
aggregate data, and once in place
can also process that data and deliver
real time analytics. Coupled with DSP
and multicore processors, intelligent
nodes and gateways can provide
more useful information back to the
cloud, reducing power consumption
and extending battery life.
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Through the advent of the Internet of
Things (IoT), there has been significant inter-
est generated in edge computing. Like Cisco
fog computing, this means putting more pro-
cessing power at the edge of the cloud, which
helps to reduce the overall power consump-
tion from the sensor node to the data centre.
This represents a significant opportunity for
embedded designers, who are demanding
more sophisticated algorithms at the sensor
and the gateway. While there has been consid-
erable activity over the last few years in apps
and software to support data centre hardware,
the move to edge computing will need a much
broader base of software, running on higher
performance embedded systems.
Hardware is becoming available in both the
embedded and industrial markets that will
address this. It generally utilises dual or quad-
core processors, such as the KeyStone system-
on-a-chip offering from Texas Instruments at
the nodes and Intel Corei7 in gateways from
suppliers like ADLINK that are capable of
handling both the sensor data and the analyt-
ics. This will be essential with tens of billions
of IoT devices expected to connect to the net-
work. Current IoT architectures tend to only
deploy analytics in a data centre context once
all the information has been collected, but as
IoT deployments increase, data will simply
not be provided quickly enough. To be truly
useful, analytics will really need to be placed
at the true edge, directly into the devices.“The
situation we have at the moment is that data
is being sent to a massive data lake where it is
not being used,” said Chad Boulanger, Global
VP of Business Development for IoT Analyt-
ics at software development company Green-
wave Systems. “As the IoT continues to grow,
this is not going to add value. The only way to
do that is to do as much as possible at the true
edge of networks - within the actual devices -
so that the machine knows that something is
wrong right there and can take appropriate
action. If the data has to travel from another
part of the network, that could have a detri-
mental impact.”
According to a report by market researchers
Gartner, there will be 20.4 billion connected
IoT devices in use globally by 2020. The sheer
quantity of data that will be transmitted from
these devices is driving adoption of edge com-
puting, where connected devices and sensors
transmit data to a local gateway device instead
of sending it back to the cloud or a designated
data centre.
Edge computing is well suited for IoT appli-
cations because it allows for quicker data ana-
lytics and reduced network traffic. Real-time
data analysis for decision making purposes is
thus possible - aiding in factory optimization,
predictive maintenance, remote asset manage-
ment, building automation, fleet management
and logistics. But edge computing is not just
about analytics. Adding more energy-efficient
methods for handling algorithms quickly and
locally can save power reserves in remote bat-
tery-based nodes, reducing the amount of
data traffic and thereby extending operational
lifespan. Using digital signal processing (DSP)
provides the ability to use more sophisticated
algorithms for analytics and data processing,
while increased memory capacity allows data
to be buffered for longer low-power states.
Flexible I/Os enable a more distributed het-
erogeneous processing architecture. This
combination provides the flexibility needed
for OEMs to quickly deliver new innovations.
The challenge is providing the right level of
performance in embedded devices. Along-
side analytics, one of the first steps is to
increase the prevalence of computer vision.
This requires more dedicated DSP blocks in
the embedded processors, as well as much
greater focus on the skills of the embedded
designer. This focus on edge processing is
also driving programmable logic technology
further into embedded sensor systems, with
products such as the iCE40 UltraPlus FPGAs
from Lattice Semiconductor leading the way.
This has eight times more memory and twice
the DSP resource, plus improved I/Os over
