Machine Learning Made Faster, More Efficient

A key subset or application of AI is Machine Learning. Most machine learning today is processed on Arm CPUs, and we continuously release new efficiency and power improvements that allow ML models to run on even the smallest endpoint devices and sensors. Arm machine learning solutions combine hardware IP, software, and an AI development framework to guide designers in building the next generation of innovative, portable AI applications for the cloud, edge, and endpoint.

Machine Learning Applications for Smarter, More Energy Efficient Devices and Apps

Today’s most disruptive organizations leverage Arm machine learning technologies to quickly and easily integrate new features across a wide range of use cases. Solutions equipped with intelligent vision, voice, and vibration capabilities have the power to advance entire industries.


Deliver immersive visuals and capture insights from intelligent cameras.

  • Image Classification
  • Object Detection
  • Image Segmentation
  • Super Resolution
  • Human Pose Estimation
  • Face Recognition
  • Depth Estimation


Enable key word detection and automated speech recognition locally on the device—with no cloud required.

  • Key Word Spotting (KWS)
  • Automatic Speech Recognition (ASR)
  • Natural Language Processing (NLP)
  • Beamforming
  • Noise Suppression
  • Machine Translation
  • Speech Synthesis


Leverage vibration to analyse signals, monitor health, predict maintenance and detect anomalies.

  • Human Activity Recognition
  • Cardiac Abnormality Detection (ECG)
  • Industrial Anomaly Detection
  • Sensor Fusion
  • Motor Control
  • Predictive Failure

Case Studies

Through our vast ecosystem, Arm already powers a wide range of devices and applications that rely on ML at the network edge and endpoints. By adding ML capabilities to processor technology, Arm is helping devices and applications become even smarter, more energy efficient, and more affordable. The result is transforming business models across a range of markets, from the edge to the enterprise.

Nota’s Automatic AI Model Compression Platform

Nota’s Automatic AI Model Compression Platform, NetsPresso, powered by Arm is bringing AI to the smallest of devices. This video features two incredible case studies on facial recognition and intelligent traffic monitoring created using Nota’s solution.

To learn more, contact Nota in our Arm AI Ecosystem Catalog.

Ignitarium Delivers Real-time Noise Suppression

Ignitarium’s real-time noise suppression software uses machine learning to suppress background noise. This exciting step in the innovation of audio and video segments designed for low-cost edge devices is enabled by the power efficiency and mature software support for machine learning functions found in Arm processors.

Emotion3D’s In-Cabin Monitoring Software

Emotion3D uses Arm-based CPUs to enable the high-accuracy, high-performance, flexible features necessary to support a range of devices that require real-time analytics. Currently, the company is using Arm processors to create AI-powered software that helps make the driving experience safer.

See all AI Case Studies

Arm AI: Bringing AI Technologies to Life

As developers enhance more and more applications with AI features, Arm AI is helping organizations leverage the right AI technologies to support these innovative new business models across industries.

Learn how Arm AI technologies integrate hardware and software, and come with a vast partner ecosystem to help bring your trailblazing ideas to life.

Learn More

Machine Learning Starts with Arm CPUs

As AI compute moves from the cloud to where the data is gathered, Arm CPU and MCU technologies are already handling the majority of AI and ML workloads at the edge and endpoints. The CPU is central to all AI systems, whether it’s handling the AI entirely or partnering with a co-processor, such as a GPU or an NPU for certain tasks.

What’s Powering Artificial Intelligence

Machine Learning is the rapidly evolving, core component of AI. After years of focusing on centralized compute farms in the cloud, developers are now looking to improve performance and balance ML functionality with security and costs. Machine learning at the edge may be the answer.

Buyer’s Guide: Selecting the best solution for your ML application

This must-read guide explores key considerations for choosing the right processor IP mix for machine learning, ensuring an optimal balance of ML system performance, cost, and product design.

Processors Designed and Optimized for Machine Learning

Ethos NPUs are used in conjunction with any of the Cortex CPUs and GPUs below. To select the best combination for your project, you must balance product functionality, cost, scalability and performance requirements.

Talk with an Expert

With so many applications for artificial intelligence emerging, it can be difficult to know where to start. Talk to an Arm expert about the right machine learning solution for your AI project.

Get in Touch

Machine Learning Resources

Arm Tech Talks

This series of talks brings you best practices and the latest trends and technologies from across the Arm ecosystem. Covering the latest cutting-edge machine learning research, real-world use cases, code examples, workshops, demos, and more.

Looking to Add ML to Your Device?

Explore platform configuration, hardware, software, and ecosystem significance. Grasp the basics of ML, explore opportunities and challenges, and learn how to get started.

Arm AI Partner Ecosystem

Arm’s extensive AI ecosystem simplifies AI deployment on intelligent endpoint devices by providing best-in-class tools, algorithms and applications to businesses worldwide.