Get started in exploring ExecuTorch on Arm Ethos-U85 by checking out the code and tools Arm has released to seamlessly deploy AI models on IoT solutions built on Arm.

Learn how Arm and Meta have collaborated to enable AI developers to deploy quantized Llama models on Arm CPUs using ExecuTorch and KleidiAI.

In this blog post, we explore a neural network based solution for improving low light video and optimizing for mobile.

Demonstrating the performance benefits of the NPU by running two TinyML applications on a modern microcontroller that is equipped with an Arm Ethos-U55 NPU.

Developing AI applications for camera trap data to non-invasively monitor bears and other wildlife.

This blog post shows the results of a research project into speaker identification using a DNN. Learn more about the key performance results, and future applications in AI tasks.

Learn about Arm KleidiCV, an open-source library of optimized routines for accelerating Computer Vision (CV) workloads on Arm CPUs. It is easily integrated into Any CV framework.

Learn all about Arm KleidiAI, an open-source library for accelerating AI performance on any AI framework. KleidiAI is designed for easily integrating optimized routines for Arm CPUs.

Large Language Models (LLMs), a form of generative AI inference, can now run on the majority of mobile devices built on the Arm CPU. This blog explains how and why.

Calling all developers who want to maximize their neural network performance! We present the Arm MLIA, the work leading up to it and why it matters.

How developers can optimize their AI models for Arm Ethos-U NPUs using the NVIDIA TAO Toolkit.

Arm has worked with Meta to introduce support for Arm platforms in ExecuTorch.

Current state of quantization on mobile (Android) is presented. Post-Training Quantization and Quantization Aware Training (QAT) in TFLite are discussed.

During the Game Developer Conference (GDC) in March 2023, we showcased our multi-agent demo called Candy Clash, which is a mobile game containing 100 intelligent agents.

Using the Unity ML-Agents Toolkit, we developed Candy Clash demo, the multi-agent system, where dozens of rabbit characters work as a team, aiming to crack their opponent's egg.

How to design a neural network so that the compiler is able to efficiently compress the weights of your ML model and benefit fully from the capabilities of the Ethos-U hardware?

Using the Unity ML-Agents Toolkit, we developed Candy Clash demo, the multi-agent system, where dozens of rabbit characters work as a team, aiming to crack their opponent's egg.

Using the Unity ML-Agents Toolkit, we developed Candy Clash demo, the multi-agent system, where dozens of rabbit characters work as a team, aiming to crack their opponent's egg.

In this blog, we demonstrate how to analyze a Machine Learning model on a layer-by-layer basis.

Join DSP Concepts and Alif Semiconductor at Arm DevSummit 2022 to discuss ML techniques commonly used for audio. Discover the features and benefits of the Audio Weaver platform.

This blog introduces how to deploy a PP-OCRv3 English text recognition model on Arm Cortex-M55 processor with Arm Virtual Hardware.