Tech Briefs Magazine - June 2022 - Sensor-22

Intelligent Sensing: Enabling the Next " Automatic Age " - Marco Cassis
porting embedded AI to the very lowpower
edge devices.
INFORMATION UPLOAD
Physical Domain
Object Information DomainCyber Cognitive Domain
Sensor
Actuators
Connectivity
Sense, think & act unit
ACTUATION & CONTROL
Data centers
Processing
(MCU, MPU)
STMicroelectronics contributed from
the start to the discussions in TinyML
together with other companies and universities.
TinyML focuses on hardware,
algorithms, and software capable of performing
on-device sensor data analytics
at extremely low power, typically in the
milliwatt range and below to enable a
variety of always-on use cases targeting
battery-operated devices.
Solutions can then be benchmarked
thanks to the MLCommons initiative,
which has a specific chapter called
TinyMLPerf.
Figure 2. Distributing intelligence in Cyber Physical Systems. (Image: STMicroelectronics)
Intelligent Sensing: Enabling the Next " Automatic Age " - Marco Cassis
Advanced Sensor Applications
Advances in photonics, MEMS, sound,
environmental, and heterogeneous
sensing all benefit from embedding AI
processing within the sensor. It will significantly
reduce noise, latency, and
package size while increasing accuracy
and stability of performance and operating
at microwatt levels of power consumption.
For
example, ST has produced an inertial
sensor with an Intelligent Sensor
Processing Unit that embeds these concepts
and is so small that it looks like a
speck on your fingertip. This Intelligent
Sensor Processing Unit, which is programmable
and has a specific instruction
set, enables algorithmic solutions
to best exploit constrained sensor resources.
Similar dedicated hardware
processing devices are also routinely
deployed in image sensors.
Figure 3. STMicroelectronics has produced an inertial sensor with an Intelligent Sensor Processing
Unit that is so small it looks like a speck on your fingertip. (Image: STMicroelectronics)
Moving more intelligence, and especially
artificial intelligence, to the Object
Domain is a challenge considering its
relatively constrained resources. Nevertheless,
the introduction of ultra-low
power technologies paired with innovative
architectures and hardware accelerators
will allow this challenge to be met.
Semiconductor companies have a key
role to play enabling AI processing at
the edge. ICs can deliver solutions with
optimal performance density and power
consumption at low cost and small size.
Machine learning will require advanced
tools and ecosystems enabling end-toend
AI solution development and security
needs to be pervasive throughout all
these steps.
22
Deploying a Neural Network on the
Cloud today is easier than trying to understand
how to partition a solution between
the object domain and the cyber
domain. But we need to make this effort
to achieve optimal solutions and minimize
our carbon footprint. In the next
few years AI " at the edge " functionality
will be embedded in most products.
These products will benefit from improved
accuracy, insights, intelligence,
privacy, and less latency thanks to the
current and next-generation sensors
and nodes of sensors.
The move of AI processing to the
edge started several years ago. In 2018
the TinyML Foundation was created by
a technical community that saw value in
www.techbriefs.com
Developing and Applying AI at the
Edge
For faster development of AI solutions
in the Cloud or at the edge, we
need tools that enable engineers to
speed up the whole process from data
collection and labelling through to optimized
deployment and validation on
the target platform.
These steps can benefit from automated
machine learning (AutoML) -
automating the tasks of applying machine
learning to real-world problems.
This supports people without machine-learning
expertise to quickly develop
solutions, thus optimizing productivity.
AutoML
includes tools that automatically
identify the best model for a specific
task, find the best configuration to
Sensor Technology, June 2022
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Tech Briefs Magazine - June 2022

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