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…

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…

Machine learning (ML) is no longer the new kid on the block. We’re almost all familiar with the concept of personal assistants, connected homes and a seemingly limitless torrent of gadgets that can improve our lives – as long as we have a data connection…

You’ve got 20 bright teenagers, a classful of robots running on Raspberry Pi Zero and an interest in neural networks (NNs). What do you do next? You dig into an Arm machine learning tutorial and use object detection to control the robots’ behaviour…

This article is part of the Arm Innovator Program, a series created to highlight the work of key technical leaders who are pushing the boundaries of how Arm architecture can enable next-generation solutions.

Meet Ibrahim Abdalkader, an embedded systems…

Overview

This blog is for embedded software developers who want to apply Machine Learning (ML) on Arm Cortex-M. We will show you how to deploy a trained Neural Network (NN) model (using Caffe as an example) on those constrained platforms with the Arm…

Deploying advanced deep learning algorithms on edge devices – especially for computer vision applications like autonomous vehicles and IoT – requires special capabilities. At Brodmann17, our mission is to create practical, neural-network based…

Augmented reality (AR) is a technology where virtual content is overlaid on top of the real world. If you’ve ever played with Snapchat “Lenses” on your mobile phone, to transform a selfie with a cartoon monocle or dog ears, you will…

Imagine you’re 30 meters down, diving above a reef surrounded by amazing-looking creatures and wondering what species the little yellow fish with the silver stripes is. You could fumble around for a fish chart, if you have one, but what you really want…

It would be really amazing to have a personal assistant in my hands that is actually smart, truly understands my words and responds intelligently to resolve day-to-day tasks. The recent advancements in Machine Learning (ML) make me optimistic that such…

Recently, we announced our neural network machine learning (ML) software, Arm NN, a key piece of technology that makes it much, much easier to build and run ML applications on power-efficient, Arm-based platforms.

In essence, the software provides a bridge…