Arm Is Advancing AI Through Faster, More Efficient Machine Learning
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 platform to help you build the next generation of innovative, portable AI applications for the cloud, edge, and endpoint.
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)
- 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
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.
The Arm AI Platform enables ultra-efficient machine learning, scalable AI and neural network functionality at every point of the performance curve. It provides a total machine learning solution that includes IP, software, tools, a vast ecosystem, and end-to-end security enablement.
This comprehensive, heterogeneous compute platform will bring your most innovative ideas to life.
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.
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.
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.
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.
AI Virtual Tech Talks
- Arm NN: Build and Run ML Apps Seamlessly on Mobile and Embedded Devices
- Optimizing Machine Learning Workloads on Power-efficient Devices
Arm Machine Learning Research