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In Application Examples, AI, Internet of Things, Our products


NANOEDGE AI STUDIO: 2 NEW ALGORITHM FAMILIES IN 1 COMPREHENSIVE AI SOLUTION

December 1, 2021



To improve the accessibility of our content, please find the audio version of
this blog post.



ST is releasing NanoEdgeTM AI Studio V3 today, an automated machine learning
tool that features two additional machine learning algorithm families,
simplified data logging, and a revamped user interface. The new software thus
grows its reach by covering more use cases and becoming more straightforward to
embedded developers. Our teams are also offering Edge AI Sprint Packages. The
bundle helps teams bootstrap their projects thanks to training sessions and tech
support, among other things. Therefore, today’s release is a testament to ST’s
desire to make machine learning at the edge accessible to all.


TABLE OF CONTENTS

 * 1 What is NanoEdge AI Studio?
   * 1.1 The Challenges Behind Creating Machine Learning Applications
   * 1.2 The Solution to Bringing Machine Learning Everywhere
 * 2 What’s New in NanoEdge AI Studio?
   * 2.1 Two New Families of Algorithms
   * 2.2 New Effortless Data-Logging Features
   * 2.3 New Graphical User Interface
 * 3 Experiencing NanoEdge AI Studio on Embedded Systems
   * 3.1 Automating Machine Learning with NanoEdge AI Studio
   * 3.2 Bootstrapping Projects with Edge AI Sprint
   * 3.3 Next Steps


WHAT IS NANOEDGE AI STUDIO?


THE CHALLENGES BEHIND CREATING MACHINE LEARNING APPLICATIONS



In 2019, the ST Blog sat with the creators of NanoEdge AI to better understand
its first machine learning application. Traditionally, large companies looking
to benefit from machine learning must hire one or more data scientists to
collect a massive amount of data for months, clean them, and create AI models.
Embedded developers then port the implementation on microcontrollers or use
tools like STM32Cube.AI to convert neural networks into optimized code for STM32
MCUs. When a company wrestles with tight budget constraints, hiring one or more
data scientists may be out of the question. Additionally, it may not be possible
to outsource the job. Some situations are sensitive, while others require
someone to be constantly on staff.

Even with the right people and all the time in the world, obtaining quality data
is still an issue. Despite all the advances in machine learning, getting
reliable training samples can be a severe problem. For instance, if an
application tries to detect abnormal behaviors, data may be unavailable. Indeed,
while many datasets work for classification problems, such as anomaly detection,
they’re useless when trying to detect new situations. It is also critical to
obtain good quality data, which is far from obvious. When samples aren’t plagued
by typos or missing information, recording clean sets and precisely labeling
them can demand serious investments.


THE SOLUTION TO BRINGING MACHINE LEARNING EVERYWHERE

A developer working on NanoEdge AI Studio

NanoEdge AI Studio is a utility that speaks to embedded developers, even to
those with no data science expertise. The magic lies in running the training
phase that learns a complex nominal behavior and the inference on the same
device. The entire process can thus run on the same STM32 microcontroller.
Additionally, the end-user interaction can be simple, like pushing a button. As
a result, engineers can customize their system to its local environment, making
it more robust and easier to install.

NanoEdge AI Studio runs on Windows 10 or Ubuntu and is the best way to process
data as well as find the most pertinent AI libraries. The application’s design
focuses on embedded development and seamless integration in C applications. Put
simply, NanoEdge AI Studio considers basic specifications like CPU, memory,
sensors, and searches for the best NanoEdge AI library. It then outputs a
library running on STM32 MCUs that developers can directly integrate into their
embedded applications. And with today’s update, the utility offers more
libraries as well as data logging capabilities.


WHAT’S NEW IN NANOEDGE AI STUDIO?


TWO NEW FAMILIES OF ALGORITHMS

Holding a Nucleo board in front of NanoEdge AI Studio

Before today’s launch, NanoEdge AI Studio supported two major machine learning
algorithms: anomaly detection and classification. With NanoEdge AI Studio V3,
these two families now benefit from a more significant number of libraries.
Moreover, we also optimized current algorithms to increase performance on
existing use cases. Hence, embedded developers may experience better resource
management or faster execution times when switching to the new software version.

The application also offers two new families of algorithms: extrapolation and
outliers. The former helps anticipate behaviors in untested conditions. Also
called regression, it maps the relation between multiple variables. For example,
data sets could measure a fan’s behavior at 100ºC, 110ºC, and 150ºC. Now, thanks
to a regression algorithm, the machine learning application can extrapolate the
behavior at 160ºC. The extrapolation algorithm in NanoEdge AI Studio doesn’t
only cover linear regressions. Indeed, it also offers more advanced analysis
techniques to tackle complex situations. As a result, developers can now create
new applications that monitor things that data scientists cannot test
themselves.

The second algorithm is an outlier detection system that rests on a single class
of values. Indeed, the system only learns normal behavior. Anything that
deviates from it becomes an anomaly. Previously, when using the anomaly
detection system, developers would record normal behavior, then simulate one or
more problems. As mentioned, it was possible to learn all behaviors on the same
microcontroller, thus vastly simplifying operations. However, in some cases,
reproducing anomalies is simply impossible. Hence, outlier detection can use
data from routine operations to infer an anomaly in such a situation.


NEW EFFORTLESS DATA-LOGGING FEATURES

Data scientists may run against the imperative to release the final product to
market and may be stuck. Indeed, while there’s no better data than the one from
real-world usage, it is not always available. Additionally, many are
time-constrained. Hence, the new data-logging feature turns any STWIN SensorTile
wireless industrial node into the most straightforward data collection tool.
Users connect the board to their PC and use NanoEdge AI Studio to switch to data
logging. Afterward, recording data becomes automatic. Engineers can fix the
STWIN board to their equipment to monitor it. The sensors will record data that
developers can then label and parse to create more accurate applications.


NEW GRAPHICAL USER INTERFACE

Another vital improvement in the new version of NanoEdge AI Studio is the user
interface. With the arrival of new algorithms and data collection features, it
was critical to improve the user experience. It was also crucial to optimize
developers’ workflow. Indeed, NanoEdge AI Studio targets teams looking to bring
machine learning to the edge. The libraries are tiny – as little as 1 KB – and
highly optimized. It was thus necessary to also improve access to algorithms to
ensure developers can easily select their project category and rapidly generate
their libraries.


EXPERIENCING NANOEDGE AI STUDIO ON EMBEDDED SYSTEMS


AUTOMATING MACHINE LEARNING WITH NANOEDGE AI STUDIO

The sensor vibration demo with a Nucleo board

Before the advent of NanoEdge AI Studio, engineers had to contact software
vendors, go over their hardware configuration, and the behavior to monitor.
Today, the tool enables developers to customize, generate, and validate their
machine learning library. The utility first asks users to select their Cortex-M
architecture and the sensor in the system. They then import a file with values
describing the equipment’s typical behavior. It can be data from an
accelerometer on a fan or the electrical information of industrial equipment.
Afterward, NanoEdge AI Studio automatically tests, optimizes, and sorts the best
algorithmic combination among hundreds of millions of possible combinations and
produces a customized library that developers can validate using the embedded
emulator.



NanoEdge AI Studio V3 now supports all ST development boards right from its user
interface. The availability of optimized and free libraries thus means that
running a proof-of-concept is straightforward. For instance, in the smart
vibration sensor tutorial, users can grab the NUCLEO-L432KC to capture a fan’s
normal behavior. They then feed the data to NanoEdge AI Studio and obtain a
library that they can call in the main loop to run a minimum number of training
cycles previously defined by benchmarks within the new software before engaging
in inference. Hence, NanoEdge AI libraries can rapidly help create applications
that use predictive maintenance, smart security operations, and more.


BOOTSTRAPPING PROJECTS WITH EDGE AI SPRINT

Many customers fail to assess and demonstrate the benefits AI will bring to
their application. Hence, to jumpstart applications on the right foot, Edge AI
Sprint brings more than just a utility but a whole support system of experts
that can guide developers through the minefields inherent to their application
and use case. Edge AI Sprint is thus a bundle that includes training sessions, a
NanoEdge AI Studio license, and technical support. Teams can select from various
license duration, depending on their projects’ complexity, to ensure they can
reach production. Meant to bootstrap a project’s first steps, Edge AI Sprint
thus limits risks and investments while increasing the chances of success.


NEXT STEPS

 * Download NanoEdge AI Studio
 * Contact your sales representative or authorized business partners to order a
   license for NanoEdge AI Studio and Edge AI Sprint


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meaningful. Get more from technology to get more from life with
STMicroelectronics.

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TouchGFX 4.19: 2 New Features That Make Working with Texts and Graphs a Breeze
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TouchGFX 4.19: 2 New Features That Make Working with Texts and Graphs a Breeze
bySTMicroelectronics
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TouchGFX 4.19: 2 New Features That Make Working with Texts and Graphs a Breeze
March 22, 2022
STMicroelectronics
Transform Health Care and Save Lives with a SensorTile.Box and a BlueCoin
March 8, 2022
STMicroelectronics
L9963E: 5 Things to Watch Out For in a Battery Management Solution
February 15, 2022
STMicroelectronics
3 Powerful Lessons from NeuronicWorks and STM32 About Designing Reliable,
Recyclable, and Energy Efficient Products
February 7, 2022
STMicroelectronics
STM32CubeProgrammer and STM32CubeMonitor: See How Power Users Get More
Productive on STM32
January 25, 2022
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