Outcomes, Insights and Best Practices from IIC Testbeds: Deep Learning Facility Tesbed
To extend the usefulness of the published testbeds in the Testbed Program of the Industrial
Internet Consortium (IIC), the Testbed Working Group has developed an initiative to interview
the contributors of selected testbeds to showcase more insights about the testbed, including the
lessons learned through the testbed development process. This initiative enables the IIC to share
more insights and inspire more members to engage in the Testbed Program.
This article highlights the Deep Learning Facility Testbed. The information and insights described
in the following article were captured through an interview conducted by Mr. Joseph Fontaine,
Former Vice President of Testbed Programs at IIC, with Brad Klenz, IoT Analytics Architect at SAS;
Ken Hatano, Chief Specialist at Toshiba; and Said Tabet, Chief Architect, Emerging Technologies
& Ecosystems, CTO Office at Dell Technologies. Brad, Ken and Said are active members in the IIC
where they serve as co-leads of the Deep Learning Facility Testbed and are key contributors to
the Testbed Working Group.
D EEP L EARNING F ACILITY T ESTBED – F ROM C ONCEPT TO R EALITY
resources and technologies from Toshiba
and Dell and the perceived needs in the
industry, the IIC Deep Learning Facility
Testbed was founded to improve building
operational efficiency and occupant
satisfaction using AI deep learning
techniques, and to share the new-found
knowledge within the IIC.
When the Deep Learning Facility Testbed
began in late 2016, no other IIC testbed dealt
explicitly with artificial intelligence (AI) or
deep learning. Dell Technologies (EMC at the
time) and Toshiba had set a goal to launch a
deep-learning-based,
technology-based
testbed. Fortunately, Toshiba owned and
operated the Toshiba Smart Community
Center,
a
state-of-the-art
building
established in 2013 in Kawasaki, Japan that
has more than 30,000 data points and
sensors. Toshiba built this brand-new
building for the purpose of experimenting,
and many Toshiba employees transferred
from their Hamamatsu-cho headquarters to
Kawasaki. One of the objectives was to
optimize maintenance. There was also a
need to increase energy efficiency through
the adjustment in all power-consuming
services in buildings while improving the
visiting customer experience and employee-
resident comfort. At that time, Toshiba also
had a department that focused on deep-
learning-specific technology. Based on the
In early 2019, SAS joined the testbed in
accordance with one of their main corporate
objectives: environmental sustainability. SAS
has experience with sustainability as a focus
area, so they came into the project with prior
knowledge on energy efficiency. When
starting down the path to make a building
more
sustainable,
many
physical
infrastructure changes, such as lighting
choices, can improve energy efficiency.
Progressing down that path leads to more
reliance on technology and analytics to
improve energy conservation continually.
There is also immediate payback associated
with more sustainable options, such as
money saved on electricity and energy bills.
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June 2019