Machine Learning Blog

Join Arm at Google Next '18 conference to see the latest secure client platforms, along with low-power machine learning at the edge.

Arm recently invested in SWIM.AI’s Series B to accelerate developers in creating the apps that will define the edge computing era.

This blog shares our experience developing our AR and ML demo using Google ARCore and Arm NN in Unity. Throughout this guide I'll point out the most important steps.

Deploying advanced deep learning algorithms on edge devices, especially for computer vision applications like autonomous vehicles and IoT, requires special capabilities.

Designers looking for an open HP cache-coherent chip to chip accelerator interface with an easy migration path from today’s PCIe should consider CCIX for their next SoC.

A number of technological challenges are posed by Augmented Reality (AR) on mobile devices. This blog reviews these, and presents a paper from Arm Research ML Group that seeks to address these challenges…

Rob Aitken is an Arm Research Fellow. Here, he shares his thoughts and key takeaways from the Future Chips Forum, held last December in Beijing.

We are proud to announce the release of two new Mali GPUs, Mali-G52 and Mali-G31, to our Mainstream and Ultra Efficient roadmaps.

Neural Network accelerators as an essential part of bringing ML to life on your device – and whilst it’s true that they have a significant role to play, they’re just one part of the story.

This tutorial shows how to use Streamline on the AlexNet example application from the Compute Library which was demonstrated in the article, Running AlexNet on Raspberry Pi with Compute Library.

We are very excited to be able to announce some of our recent collaborations in mobile and camera technologies, some of which we will be showcased at the Mobile Word Congress 2018 in Barcelona.

We are launching Project Trillium to kickstart a new wave of invention in the world of artificial intelligence (AI), of which machine learning is a key part.

Arm NN SDK, a key piece of technology that makes it much, much easier to build and run ML applications on power-efficient, Arm-based platforms.

The Arm ML processor is a brand new design for the mobile and adjacent markets - such as smart cameras, AR/VR, drones, medical, consumer electronics

What would you do with a Cortex-M4, a neural network, some Lego and cable ties? Sebastian Förster built a small, four-legged robot and taught it to walk.

If you’d like to develop your Convolutional Neural Networks using just the Compute Library and a Raspberry Pi, this step-by-step guide will show you how…

In this blog, I will try to use a sample Convolutional Neural Network (CNN) as an example to descript how to use Arm development solutions to enable Machine Learning (ML) on device.

Arm now supports Android Neural Networks API (NNAPI) with open-source, optimized NN operators that deliver serious performance uplift across CPUs and GPUs