Machine learning possible on microcontrollers

ARM’s Zach Shelby introduced the use of microcontrollers for machine learning and artificial intelligence at the ECF19 event in Helsinki on last Friday. The talk showed that that artificial intelligence and machine learning can be applied to small embedded devices in addition to the cloud-based model. In particular, artificial intelligence is well suited to the devices of the Internet of Things. The use of machine learning in IoT is also sensible from an energy efficiency point of view if unnecessary power-consuming communication can be avoided (for example local keyword detection before sending voice data to cloud more more detailed analysis).

According to Shelby , we are now moving to a third wave of IoT that comes with comprehensive equipment security and voice control. In this model, machine learning techniques are one new application that can be added to previous work done on IoT.

In order to successfully use machine learning in small embedded devices, the problem to be solved is that it has reasonably little incoming information and a very limited number of possible outcomes. ARM Cortex M4 processor equipped with a DSP unit is powerful enough for simple hand writing decoding or detecting few spoken words with machine learning model. In examples the used machine learning models needed less than 100 kilobytes of memory.

zackdscf6473

The presentation can be now viewed on YouTube:

Important tools and projects mentioned on the presentation:

TinyML

TensorFlow Lite

uTensor (ARM MicroTensor)

TensorFlow Lite Micro

Articles on presentation:

https://www.uusiteknologia.fi/2019/05/20/ecf19-koneoppiminen-mahtuu-mikro-ohjaimeen/

http://www.etn.fi/index.php/72-ecf/9495-koneoppiminen-mullistaa-sulautetun-tekniikan

 

318 Comments

  1. Tomi Engdahl says:

    During this recent tinyML Foundation Talk, Manivannan Sivan explored the potential of embedded ML in Industry 4.0. His approach to predictive maintenance employs the Portenta H7 and Edge Impulse models to detect anomalous operation in industrial equipment like pumps, valves, and fans.
    https://www.youtube.com/watch?v=4K3D5Ano8VA

    Reply
  2. Tomi Engdahl says:

    EdenOff is a Nano 33 BLE Sense-based device that can be placed inside of a wall outlet to predict power outages using a tinyML model.

    This Arduino device can anticipate power outages with tinyML
    https://blog.arduino.cc/2022/05/24/this-arduino-device-can-anticipate-power-outages-with-tinyml/

    Bandini then deployed this model to a DIY setup by first connecting a Nano 33 BLE Sense with its onboard temperature sensor to an external ZMPT101B voltage sensor. Users can view the device in operation with its seven-segment display and hear the buzzer if a failure is detected. Lastly, the entire package is portable thanks to its LiPo battery and micro-USB charging circuitry.

    https://studio.edgeimpulse.com/public/90995/latest

    Reply
  3. Tomi Engdahl says:

    Run Machine Learning Code in an Embedded IoT Node to Easily Identify Objects
    https://www.digikey.com/en/articles/run-machine-learning-code-in-an-embedded-iot-node?dclid=CLy-84zUhvgCFZdNwgodJbAMmw

    Internet of Things (IoT) networks operating in dynamic environments are being expanded beyond object detection to include visual object identification in applications such as security, environmental monitoring, safety, and Industrial IoT (IIoT). As object identification is adaptive and involves using machine learning (ML) models, it is a complex field that can be difficult to learn from scratch and implement efficiently.

    The difficulty stems from the fact that an ML model is only as good as its data set, and once the correct data is acquired, the system must be properly trained to act upon it in order to be practical.

    This article will show developers how to implement Google’s TensorFlow Lite for Microcontrollers ML model into a Microchip Technology microcontroller. It will then explain how to use the image classification and object detection learning data sets with TensorFlow Lite to easily identify objects with a minimum of custom coding.

    It will then introduce a TensorFlow Lite ML starter kit from Adafruit Industries that can familiarize developers with the basics of ML.

    Reply
  4. Tomi Engdahl says:

    Predictive Maintenance Of Compressor Water Pumps
    https://hackaday.io/project/185930-predictive-maintenance-of-compressor-water-pumps

    Applying sensor fusion with RSL10 and Bosch sensors to run a TinyML model for predictive maintenance of compressor water pumps.

    Reply
  5. Tomi Engdahl says:

    Tic-Tac-Toe Game with TinyML-based Digit Recogniti
    https://hackaday.io/project/185957-tic-tac-toe-game-with-tinyml-based-digit-recogniti

    Play Tic-Tac-Toe (also known as Xs and Os) using handwritten digits recognized with the help of TinyML techniques.

    Reply
  6. Tomi Engdahl says:

    Edging Ahead When Learning On The Edge
    https://hackaday.com/2022/06/21/edging-ahead-when-learning-on-the-edge/

    “With the power of edge AI in the palm of your hand, your business will be unstoppable.”

    That’s what the marketing seems to read like for artificial intelligence companies. Everyone seems to have cloud-scale AI-powered business intelligence analytics at the edge. While sounding impressive, we’re not convinced that marketing mumbo jumbo means anything. But what does AI on edge devices look like these days?

    Reply
  7. Tomi Engdahl says:

    Detecting harmful gases with a single sensor and tinyML
    https://blog.arduino.cc/2022/07/11/detecting-harmful-gases-with-a-single-sensor-and-tinyml/

    Experiencing a chemical and/or gas leak can be potentially life-threatening to both people and the surrounding environment, which is why detecting them as quickly as possible is vital. But instead of relying on simple thresholds, Roni Bandini was able to come up with a system that can spot custom leaks by recognizing subtle changes in gas level values through machine learning.

    Reply
  8. Tomi Engdahl says:

    SparkFun Launches Arducam Pico4ML-Powered Machine Learning and AI Concept Kit
    New kit is designed to introduce core concepts while building practical projects using TensorFlow Lite and Edge Impulse Studio.
    https://www.hackster.io/news/sparkfun-launches-arducam-pico4ml-powered-machine-learning-and-ai-concept-kit-6c52884fab7d

    Reply

Leave a Comment

Your email address will not be published. Required fields are marked *

*

*