AI and ML blog

In this blog, read about what COVID-Net is doing in embedded healthcare.

Read this blog from Alexander Eroma, head of Intelligence at Octonion about collaborative learning with Arm MCUs.

In this blog, read about TinyML and the optimization of TVM.

Join Arm's AI virtual tech talk series covering the latest trends in machine learning development, including deployment, optimization, best practices, as well as new ML technologies shaping the industry…

In this blog, read the steps in order to build Bazel and TensorFlow on AArch64.

Read about the emergence of AI and deep learning on embedded devices and platforms.

Read about how to train a TinyML model to recognize sounds.

A blog about machine Learning that discuss some ideas and experiences that might get you interested in creating your own ML app.

Read about an exciting new plugin framework with Arm NN.

Read about the work to maximize performance using the Arm v8 architecture. Specifically, by taking advantage of the NEON (DSP instruction) and a Floating-Point Unit (FPU).

TensorFlow is powering everything from data centers to edge devices, across industries. With TensorFlow 2.0 and the evolving ecosystem of tools and libraries, it is doing it all so much easier. At this…

The new Ethos-N NPU series addresses the performance needs of the most demanding ML use cases to deliver a superlative UX without compromising battery life.

Announcing an ML how-to guide which gives an end-to-end solution on using the Arm NN SDK. It acts as a great starting point for mobile app developers who want to trial the Arm NN SDK.

So you know your ML from your AI? Great. Want to know more about training and inference? Read on...

The next revision of the Armv8-A architecture will introduce Neon and SVE vector instruction designed to accelerate Neural Networks using the BFloat16 format.

You’ve got a great little edge device, and you’re keen to add machine learning capabilities to assist local decision-making. So, what do you do next?

Arm is pleased to announce a new online training topic, Machine Learning using Arm. It covers Arm’s IP solutions for optimizing ML applications for Arm hardware.

CPUs and GPUs are ML powerhouses in their own right, but the Arm ML processor delivers the most efficient processing for an exciting UX on edge devices.

Machine learning is changing the way we interact with technology. Arm's Chris Shore talks to Imagimob about their intelligent software solutions with actionable insights in real-time.

Google partners with Arm to develop TensorFlow Lite Micro; uTensor – an inference library based on Arm Mbed and TensorFlow –  becomes part of the project.

Discover why on-device machine learning is here to stay – and what it's already doing for you.