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Our everyday lives generate huge amounts of data and information – digital, biological, physical, and sensorial. With advances in AI, this data can be used to create incredible benefits for humankind. To realize this challenge and extract useful information…

Arm NN enables third parties to run neural network models on their custom inference hardware. It also allows the addition of new backends through the new ‘Pluggable Backend’ mechanism.

Arm NN's architecture has been extended to make a new…

Recurrent Neural Networks (RNNs) are an important class of algorithms. They are used in tasks where the strict order of the input conveys certain information, for example, natural language processing (NLP) and time-series based data. Increasingly, these…

We are very pleased to announce the launch of a machine learning how-to guide – Deploying a quantized TensorFlow Lite MobileNet V1 model.

The guide provides an end-to-end solution on using the Arm NN SDK. It walks you through creating a program which…

Neural Networks are a key component of Machine Learning (ML) applications. Project Trillium, Arm’s heterogeneous ML platform, provides a range of technologies in this field, including instructions that accelerate such applications running on CPUs based…

Imagine you have a dog. Let’s call her Bingo. You’d like Bingo to fetch her leash whenever you say, “Walkies!” So what do you do? You train her. Maybe you wave the leash and say, “Walkies!” Next time, you might repeat the command word, while pointing…

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, and object tracking and localization. Furthermore…

The Arm ML Research Lab explores cutting edge techniques and state-of-the-art algorithms. One of our primary research thrusts is investigating ways to bring more machine learning applications to Arm's products, and make existing applications more efficient

We are very pleased to announce a new online training topic - Machine Learning using Arm.

About the course

This training topic covers essential information on Arm’s IP solutions for optimizing Machine Learning (ML) applications for Arm hardware. The…

Today, I’ve teamed up with Ram Cherukuri of MathWorks to provide an overview of the MathWorks toolchain for machine learning (ML) and the deployment of embedded ML inference on Arm Cortex-A using the Arm Compute Library.

MathWorks enables engineers…

OK. Quick survey: How many connected devices do you own?

Whether you’re a gadget addict or just an average Josephine, I’m not sticking my neck out too far if I guess that you own more today than you did five years ago. From smartphones and tablets to…

Yesterday, at Google I/O, Google announced that they are partnering with Arm to develop TensorFlow Lite Micro and that uTensor – an inference library based on Arm Mbed and TensorFlow –  is becoming part of this new project. (See the Mbed b…

Mobile computing is on the rise, and currently moving into some really exciting new applications and form factors ­– augmented reality (AR) glasses, unmanned aerial vehicles (UAVs), automated driver assistance systems (ADAS) in automobiles, and more.…

Nowadays you can’t turn on the news without hearing talk of artificial intelligence (AI) and machine learning (ML) – they’re the buzzwords that just won’t die. And with good reason: they’re already transforming our lives in areas as disparate as finance…