Tech Briefs Magazine - June 2022 - Sensor-23

train the model itself, and help find the
best trade-off among performance, cost,
and accuracy on a given dataset based
on project goals.
Learning the AI model parameters
directly on the device means interleaving
learning and inference phases. It
enables
the determination of neural
network model weights on the same device
that runs the model inference -
decreasing effort spend on data preparation.
On-device
learning at the edge enables
federated learning, which is a
process where edge devices exchange
results obtained locally and update their
models by using results collected from
the other devices.
It also supports reinforcement learning,
which adds the capability to quickly
react and adapt to unforeseen events
and take accurate, fast decisions.
Power and Latency
Almost half of the total power used
for inference execution is spent on moving
data between on-chip memories and
the logic used for computations. This
memory bottleneck leads to high energy
and latency costs.
An approach to overcome this limitation
is to move the computation partially
or completely to the memory and
to add accelerators and specialized
hardware to the ICs performing the AI
processing. This can be achieved thanks
to the introduction of neuromorphic
architectures and in-memory computing
technologies.
Actuators
There is an evolution towards ever
more intelligent actuators that are more
compact, more efficient, and more flexible.
This is driving emerging technologies
such as specialized microcontrollers
with new sensors and new analog frontend
solutions for a more flexible system.
Motors are the most used actuators in
the industrial world so adding intelligence
to them delivers high value. For
motion control, the industry is moving
from the classical mathematical solutions
to a neural-type approach. This
will improve response, speed, and the
ability to efficiently control nonlinear
systems, while controlling undesirable
effects including torque ripple.
Moreover, the neural-based approach
offers the opportunity for low-complexSensor
Technology, June 2022
Emerging technologies
* High performance specialized MCU
* Integration: new power modules
* Integration: new all-in-one modules
Intelligent Sensing: Enabling the Next " Automatic Age " - Marco Cassis
Innovation points
* More compact
* More efficient
* More flexible
Figure 4. There is an evolution towards ever more intelligent actuators that are more compact, more
efficient, and more flexible.
ity, online learning, so that the system
will adapt itself to changing conditions
of the motor like stress and aging.
Connectivity
Cyber Physical Systems need multiple
communications solutions. Today many
such solutions coexist to address different
needs such as throughput, coverage,
and mobility.
Different markets
have different
needs but there is convergence among
them. We see two coexisting trends.
One is toward higher throughput and
lower latency. The other is
co-integration of sensor and intelligence.
These include die stacking,
chip-scale-packaging (CSP), and heterogeneous
technology integration, among
others, that a company like ST is well
suited to develop and offer.
In Conclusion
The next automation age is not a
towards
lower throughput, shorter transmission
time, and lower power consumption.
Merging solutions for these trends will
allow more efficient end-to-end connectivity
solutions for Cyber Physical
Systems.
Increased power efficiency is a common
challenge for both trends and this
can be tackled both at system and device
level. One example is the development
of wake-up radios, with two radios at
system level. This reduces the power
consumption of the main radio while
ensuring an acceptable latency level of
the system.
At device-level, low power is achieved
through new system-on-chip (SoC) architectures
that achieve ultra-low power
dissipation in active and standby modes
- about 10 milliamps when transmitting
and receiving and about 10 microamps
in standby.
Finally, there are many technology
and packaging advances to enable the
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far-off reality - it is already here. The
number of automation-age devices is
growing rapidly, thanks to the availability
of all the key elements of Cyber
Physical Systems. However, the increasingly
complex functionality of
these devices requires more data
bandwidth and more powerful cloud
infrastructure.
This comes with data privacy, latency,
and energy consumption concerns. One
important trend to address this, is supported
by technologies that distribute
intelligence, such
as AI processing,
within the CPS.
This brings analytic capabilities close
to, or into, the sensors, allowing the
implementation of sophisticated actions
with local smart control and protecting
data privacy, as well as lowering latency
and power consumption. There are
solutions already available for this,
which are evolving rapidly to bring further
optimization.
This article was written by Marco Cassis,
President, Analog, MEMS, and Sensors
Group at STMicroelectronics (Geneva,
Switzerland). For more information, contact
Mr. Cassis at michael.markowitz@st.com or
visit http://info.hotims.com/82322-165.
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Tech Briefs Magazine - June 2022

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