In this blog, read about what COVID-Net is doing in embedded healthcare.
Unlocking the power of collaborative learning on Arm MCUs
Read this blog from Alexander Eroma, head of Intelligence at Octonion about collaborative learning with Arm MCUs.
TinyML: TVM Taming the Final (ML) Frontier
In this blog, read about TinyML and the optimization of TVM.
Introducing Arm’s AI Virtual Tech Sessions for Software Developers
Join Arm's AI virtual tech talk series covering the latest trends in machine learning development, including deployment, optimization, best practices, as well as new ML technologies shaping the industry…
Building Bazel and TensorFlow 2.x on AArch64
In this blog, read the steps in order to build Bazel and TensorFlow on AArch64.
Unlocking AI on Arm Microcontrollers with Deep Learning Model Optimization
Read about the emergence of AI and deep learning on embedded devices and platforms.
Train a TinyML model to recognize sounds that uses 23 kB of RAM
Read about how to train a TinyML model to recognize sounds.
An Introduction to Machine Learning on Mobile
A blog about machine Learning that discuss some ideas and experiences that might get you interested in creating your own ML app.
Using Arm v8 for Vision at the Edge
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 World PyBadge
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…
Optimizing AI workloads with Ethos NPUs
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.
New Arm ML guide: Deploying a quantized TensorFlow Lite MobileNet V1 model
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.
AI Basics: Training vs Inference – What’s the Difference?
So you know your ML from your AI? Great. Want to know more about training and inference? Read on...
BFloat16 processing for Neural Networks on Armv8-A
The next revision of the Armv8-A architecture will introduce Neon and SVE vector instruction designed to accelerate Neural Networks using the BFloat16 format.
Powering the Edge: How Will YOU Do ML?
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?
New online training course - Machine Learning using Arm
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.
Arm Ethos-N ML Inference Processors: Powering Exciting User Experiences on Edge Devices
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.
Accelerating innovation: transforming businesses through smart sensing and AI
Machine learning is changing the way we interact with technology. Arm's Chris Shore talks to Imagimob about their intelligent software solutions with actionable insights in real-time.
Why Google’s TF Lite Micro Makes ML on Arm Even Easier
Google partners with Arm to develop TensorFlow Lite Micro; uTensor – an inference library based on Arm Mbed and TensorFlow – becomes part of the project.
Living on the Edge: Why On-Device ML is Here to Stay
Discover why on-device machine learning is here to stay – and what it's already doing for you.