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…

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…

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…

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…

Machine Learning (ML) is moving to edge no doubt. A lot of embedded devices want to have edge computing capability to provide more advanced services, like voice recognition for smart speaker and face detection on surveillance camera. Constructing a solid…

The launch of Arm support for the Android Neural Networks API (NNAPI) sees the release of open-source, optimized neural network operators that deliver significant performance uplift across CPUs and GPUs.

Back in May at Google I/O, we heard the first public…