Smart Industry 2/2018

Omni
A Smart Helmet for Cycling
Kortiq
AIScale – Universal CNN
Hardware Accelerator
Deep learning and deep neural
networks (DNNs) are currently two
of the most intensively and widely
used predictive models in the field
of machine learning. DNNs are not
a new concept, but after the recent
initial breakthrough applications of
DNNs in the fields of speech recognition
and image recognition, as well as
due to the availability of large training
data sets and extensive compute and
memory capability in the cloud, they
have returned to the focus of both
academic and industrial communities.
Today, different types of DNNs are
being employed in areas ranging from
autonomous driving and medical
and industrial applications to playing
complex games. In many of these
applications, DNNs and, in particular,
convolutional neural networks (CNNs)
are now able to outperform humans.
This arises from their ability to automatically
extract high-level features
from large amounts of raw sensory
data during training in order to obtain
an effective representation of an input
space. This approach differs from
earlier machine learning attempts using
manually crafted features or rules
designed by experts.
“We recognize a strong and increasing
demand for object recognition and
image classification applications, says
Michaël Uyttersprot, Technical Marketing
Manager for Avnet Silica. “CNNs are
widely implemented in many embedded
vision applications for different
markets, including the industrial, medical,
IoT, and automotive market.”
CNNs are a type of feed-forward artificial
neural network in which the connectivity
pattern between the neurons
is inspired by the neural connectivity
found in visual cortex of animals and
humans. Individual neurons from visual
cortex respond to stimuli only from
a restricted region of space, known
as receptive field. Receptive fields of
neighboring neurons partially overlap,
spanning the entire visual field.
However, the superior accuracy
of CNNs comes at the cost of their
high computational complexity. All
CNNs are extremely computationally
demanding, requiring billions of computations
in order to process single
input instance. The largest CNNs (from
the VGG neural networks models)
require more than 30 bn computations
to process one input image. This
significantly reduces the use of CNNs
in embedded/edge devices.
All CNNs are extremely memorydemanding,
requiring megabytes
of memory space for storing CNN
parameters. For example, VGG-16
CNN has more than 138 m network
parameters. With a 16-bit fixed-point
representation, more than 276 Mbytes
of memory must be allocated just for
storing all network parameters.
Kortiq GmbH, a Munich-based
company, has recently developed
a novel CNN hardware accelerator,
called AIScale. AIScale, distributed as
an IP core implemented using FPGA
technology, provides high processing
speed and low power consumption.
Kortiq’s AIScale accelerator is designed
to process pruned/compressed CNNs
and compressed feature maps – this
increases processing speed by skipping
all unnecessary computations,
and reduces memory size for storing
CNN parameters as well as feature
maps. All these features help to
reduce power consumption. It is also
to support all layer types found in
today’s state-of-the-art convolution,
pooling, adding, concatenation, fullyconnected
(CNN). This yields a highly
flexible and universal system, which
can support CNN architectures without
modifying underlying hardware
architecture. It is designed to be highly
scalable, by simply providing more
or fewer compute cores (CCs), the
core processing blocks of the AIScale
architecture. By using an appropriate
number of CCs, different processing
power requirements can be easily met.
Coros makes cycling safer and smarter with a new helmet
called Omni. It is designed to help riders get the most
enjoyment, freedom, and awareness out of their rides and
enhances safety and performance at the same time. The
helmet uses bone conduction technology placed on the
helmet straps and allows the rider to hear without the
safety issues of using ear buds. Bone conduction sends
small vibrations directly to the inner ear and bypasses
the ear canal, leaving the ear completely open and aware
of external noises such as cars or conversations with
fellow riders. The helmet also has a microphone near the
forehead for two-way communications. Omni also ships
with a wireless smart remote so riders can keep their
eyes on the road and hands on the bars while controlling
media and calls with the tap of a button. The helmet has
a USB chargeable battery
with 8+ hour battery life.
In addition, it includes an
emergency alert system
that is triggered when the
G-sensor senses significant
impact, sending an
alert with GPS notification
to a designated contact.
The price of the smart
helmet is €200.
IKEA
A Smart Plug at a Low Price
from IKEA
Ikea appears to be expanding its Trådfri line of smart
lighting products, adding new smart plugs that can
make any offline product controllable via an app or
a remote control, according to Swedish tech blog
Teknikveckan. The new smart plug will reportedly come
in two versions: a “Control outlet kit” with an on / off
remote is $15 and the “Wireless control outlet” will be
$10. The power button remote can attach magnetically
to metal surfaces and has a range of 10 meters. There
will also be a package including a physical remote
control at a price of €20. Like other Trådfri products,
the new plug will most likely also support Alexa, Apple
HomeKit and Google Assistant.
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