HP Innovation Journal Issue 12: Summer 2019 | Page 14
DRIVING EFFICIENCY THROUGH TECHNOLOGY
While megatrends help us better anticipate global shifts,
resource constraints, and customer needs, it’s disruptive technol-
ogies that enable us to innovate and reshape our future.
Digital manufacturing, including 3D
printing, for example, can help to
reduce waste and energy emissions
in rapidly growing urban areas, where
transporting materials and waste is
both costly and inefficient.
It also has the potential to make a significant impact on
energy consumption. Traditional manufacturing consumes
about one-third of worldwide energy production. 9 If you
apply digital manufacturing, including 3D printing, to the
full life cycle of manufacturing—design, transportation,
production, inventory, etc.—you have a chance to substan-
tially reduce that energy use.
The last technology trend we feel will drive efficiency is
the concept of virtual machines or digital twins. When
machines can learn and respond to the data they sense and
capture, it becomes possible to create virtual models of the
machines. If we can do our development and testing on
the digital twin, and then final deployment on the actual
machine, we complete the entire process faster and more
efficiently. We can take this a step further and hook these
virtual machines together to optimize complex physical
systems and processes before any setup takes place. Compa-
nies are already starting to deploy this technology and it will
only become more useful as more data becomes available
and models become better at simulating the real world.
F See “Edge of Computing/Energy Efficiency”
article in this issue to learn more. P.62
Edge computing advancements in silicon allow for data pro-
cessing with AI and machine learning inference to happen
locally, erasing the need for data transmission and the
higher energy use that transmission requires.
Those energy-efficient compute architectures are also chang-
ing the nature of software development. Instead of spending
hours on coding, a software engineer could curate data for a
task or series of tasks, build a model, and then deploy it. For
example, instead of an HP software engineer writing firm-
ware for a 3D printer, they would collect the data that comes
from the thousands of printer sensors and actuators—which
nozzle gets fired, loader gets turned, heat sensor reading, etc.
They would then send that data to the new machine learning
chip. The machine learning chip learns the data model, and
then it is run as an inference on the printer. All the engineer
did was curate the data. This leads to fewer bugs, lighter-
weight code, and a more efficient code base. The industry
name for this type of development is Software 2.0. It’s what
Tesla uses to deliver autonomous driving.
9. The Outlook for Energy: A View to 2040,” ExxonMobil 2018;
Leendert A. et al; Runz, H. et al
12
HP Innovation Journal Issue 12