Machine Learning Blog

Microcontrollers (MCUs) are the ubiquitous computer of our time, being tiny, cheap, and low power. Often powered by using solar cell, they are in your watch, fridge, and your car will contain about thirty…

Recurrent Neural Networks (RNNs) are used in tasks where the strict order of the input conveys certain information, and so are classed as an important algorithm. These networks are being deployed on resource…

University engagements play a significant role in our partnerships at Arm Research, helping us extend our reach and build understanding of technologies as they emerge. Harvard University is one of those…

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.

Blog explores the features of the brand new Arm Mali-G57 GPU for mainstream smartphones and home appliances

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.

Increasingly, intelligent applications are using neural networks at their core to deliver new functionality to users. These applications include language understanding and translation, image recognition…

One the primary research thrusts of our ML Research Lab is investigating ways to bring more machine learning applications to Arm's products, and to make existing applications more efficient.

An outline of different machine learning use cases on devices and what workloads take place on the CPU, GPU and NPU

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.

MathWorks enables engineers to get started quickly and makes ML possible without having to become an expert. Learn about the power of MATLAB for deploying ML on Arm Cortex-A.

Introducing the Arm Mali-G77 GPU with new Valhall architecture that brings big-step change in premium mobile performance and efficiency.

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.