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TechXchange: TinyML - Machine Learning for Small Platforms

Aug. 16, 2024

AI and machine learning, including tinyML, can run on microcontrollers and small SoCs.

Artificial intelligence and machine learning (AI/ML) are being employed in a wide range of embedded applications from fine-tuning images in a smartphone to doing predictive maintenance for a motor-control application.

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Arm Bets Big on AI with New Mobile CPU Cores

The combination of X925, A725, and A520 cores, Arm’s most powerful mobile CPU cluster for AI to date, brings 46% better performance than CPU clusters based on the Cortex-X4 released...

Machine Learning

Can tinyML Bring Machine Learning to the Masses?

Delivering smarter smarts to edge and free-standing devices, tinyML can be part of a broader AI/ML deployment.

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TinyML - What is it?

TinyML is a concept rather than an AI/ML platform and one that the TinyML Foundation highlights. A number of different frameworks address this space, such as TensorFlow Lite. These articles take a look at various aspects of tinyML.

Machine Learning

Can tinyML Bring Machine Learning to the Masses?

Delivering smarter smarts to edge and free-standing devices, tinyML can be part of a broader AI/ML deployment.

Artificial Intelligence Dreamstime Daniil Peshkov 229665158

Automation

Developing Neuromorphic Devices for TinyML

The current generation of neural networks doesn’t analogously reflect the actual operation of the brain, which has led neuroscientists to research networks that more closely resembled...

Industrial

tinyML Summit: From Concept to Reality

Check out the sessions from this year's tinyML Summit.

Automation

What’s the Difference Between Machine Learning and TinyML?

Machine learning, or ML, gave us TinyML. What are the differences between the two, and what makes them unique?

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TinyML Hardware - Small AI/ML Micros

TinyML can run on standard microcontrollers, but ones with hardware acceleration or AI/ML-enhanced instruction sets can implement AI/ML models more efficiently. They can also make applications practical that would not work with a non-enhanced platform.

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Arm Bets Big on AI with New Mobile CPU Cores

The combination of X925, A725, and A520 cores, Arm’s most powerful mobile CPU cluster for AI to date, brings 46% better performance than CPU clusters based on the Cortex-X4 released...

Embedded

Arm Unveils Its Most Compact AI-Capable Cortex-M CPU

The Cortex-M52 forms a family with the M85 and M55, all of which are based on Arm’s Helium technology.

Resources

Arm Cortex-M85 Based MCU with Helium and TrustZone for Motor Control

A brief overview of the RA8T1 high performance 480 MHz MCUs that deliver real-time control for motors and power supplies in industrial automation, building automation and smart...

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Enter the Cortex-M85

Arm’s Total Solutions for IoT welcomes the high-end Cortex-M85, which further enhances AI/ML capabilities within the ecosystem.

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Cortex-M Gets Heavy-Duty Machine Learning

Like many, Arm is moving into machine learning in a big way. Its new Cortex-M55 and Ethos-U55 bring machine-learning acceleration to microcontrollers.

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Edge Computing and TinyML

Edge computing refers to running AI/ML without needing connections to the cloud to operate. TinyML solutions are those that can run at the edge using low-end computing solutions. These may, but need not always, incorporate hardware acceleration.

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11 Myths About Inference Acceleration

The inference-acceleration market has heated up dramatically, and in turn, it’s led to many misconceptions circulating amongst ill-informed vendors and customers. This article...

Kit Close-up

Tiny AI Cube Recognizes Audio Commands and Camera Images

Maxim's AI Cube packs in dual MAX78000 AI accelerators into this miniature reference design kit.

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IoT

Making Machine-Learning Design Practical for the Edge

Bringing AI to embedded devices at the edge hasn’t been for the faint-hearted. However, that’s about to change.

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The Essentials of AI Edge Computing

NXP's Robert Oshana discusses issues regarding machine learning at the edge.

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About the Author

William G. Wong | Senior Content Director - Electronic Design and Microwaves & RF

I am Editor of Electronic Design focusing on embedded, software, and systems. As Senior Content Director, I also manage Microwaves & RF and I work with a great team of editors to provide engineers, programmers, developers and technical managers with interesting and useful articles and videos on a regular basis. Check out our free newsletters to see the latest content.

You can send press releases for new products for possible coverage on the website. I am also interested in receiving contributed articles for publishing on our website. Use our template and send to me along with a signed release form.

Check out my blog, AltEmbedded on Electronic Design, as well as his latest articles on this site that are listed below.

You can visit my social media via these links:

I earned a Bachelor of Electrical Engineering at the Georgia Institute of Technology and a Masters in Computer Science from Rutgers University. I still do a bit of programming using everything from C and C++ to Rust and Ada/SPARK. I do a bit of PHP programming for Drupal websites. I have posted a few Drupal modules.

I still get a hand on software and electronic hardware. Some of this can be found on our Kit Close-Up video series. You can also see me on many of our TechXchange Talk videos. I am interested in a range of projects from robotics to artificial intelligence.