Machine Learning Blog

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.

We're excited to announce some of our recent collaborations in mobile and camera tech, some of which we will be showcased at the MWC 2018.

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.

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.

Develop your Convolutional Neural Networks using just the Compute Library and a Raspberry Pi with this step-by-step guide.

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

Artificial Intelligence is still in the early stages of adoption, but it is clear that Machine Learning will move to the edge of the network to enable new use cases and drive innovation.

CMSIS-NN is a library of neural network kernels that maximizes the performance & efficiency of DNNs on Cortex-M-based platforms for smart IoT edge devices.

It is possible to optimize neural network architectures to fit within the memory and compute constraints of microcontrollers without sacrificing accuracy.

Our predictions about how the key themes of AI and Machine Learning (ML), IoT, security and blockchain are likely to begin to make a real difference in the way all of us interact with technology in 20…

Amiko Respiro smart asthma inhalers use machine learning and Arm low-power Cortex-M processors to montior and improve treatments - and change lives.

Part 2: Highlights from Data Science Africa 2017, sponsored by Arm. Great examples of IoT & Machine Learning deployed to solve real-world problems.

Part 1: ArmMbed is a gold sponsor of Data Science Africa 2017 in Arusha, Tanzania. Here's how we got there and why. Also featuring a smart office plant!