Machine Learning on Arm

Artificial Intelligence (AI) unleashes a new wave of innovation, powering new applications and business models in multiple markets. The Arm hardware and software technologies ecosystem enables the development of intelligent, distributed, heterogeneous, and secure solutions.

Project Trillium

Project Trillium, Arm’s Machine Learning (ML) platform, enables a new era of advanced, ultra-efficient inference at the edge. Specifically designed for ML and neural network (NN) capabilities, the architecture is versatile enough to scale to any device, from IoT to connected cars and servers.

Dev Day 

Learn about some of the key building blocks needed to develop intelligent machines.

Dev Day on demand

Arm Cortex-A Series

The Arm Cortex-A series of applications processors provide a range of solutions for devices undertaking complex compute tasks.
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Arm Cortex-R Series

The Arm Cortex-R series provides a range of processors optimized for high performance, hard real-time applications.
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Arm Cortex-M Series

The Arm Cortex-M series contains the smallest/lowest power processors build by Arm, optimized for discrete processing and microcontrollers.
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Arm NN

Arm NN translates existing neural network frameworks, allowing them to run efficiently across Arm Cortex-A CPUs and Arm Mali GPUs.
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Arm Compute Library

The Arm Compute Library is a collection of low-level functions optimized for Arm Cortex-A CPUs and Arm Mali GPUs, targeting computer vision and ML.
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Be part of our ecosystem

We are growing our partnerships for machine learning solutions and would love for you to be a part of it. Get in touch with us to learn more and start promoting your solutions here.

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Life-changing asthma care

One in 12 people suffer from asthma. The Arm-powered Amiko Respiro uses machine learning to make treating and living with the condition easier.


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A cure for loneliness

Half of all elderly adults living alone report suffering from loneliness. Intuition Robotics’ social robot, ElliQ, uses machine learning and cloud robotics to encourage an active and engaged lifestyle.


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Cough monitor

Hundreds of millions of people worldwide live with chronic respiratory disorders. The Quvium Sonasure cough monitor uses machine learning to track early symptoms, inform treatment plans, and send alerts if emergency action is needed.

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Developer material

Arm is creating the tools you need to bring your best solutions to market. Find out more below.

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How-to guides

Learn to develop machine learning applications using Arm-based products and tools with our how-to guides.

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Webinars

Discover tips and techniques for your Arm-based machine learning projects with our growing bank of webinars.

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White papers

To learn more about machine learning on Arm, see our range of available white papers and research papers.

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Community Blogs

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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.

1 months ago by Darren Doyle
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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...

1 months ago by Hellen Norman
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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.

1 months ago by Nigel Stephens
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Alpha-Blending: Quantizing networks without using the STE

Increasingly, intelligent applications are using neural networks at their core to deliver new functionality to users. These applications include language understanding and translation, image recognition, and object tracking and localization. To…

2 months ago by Matthew Mattina
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Taking Constrained ML to the Next Level

One the primary research thrusts of our ML Research Lab is investigating ways to bring more machine learning applications to Arm's products, and to make existing applications more efficient.

2 months ago by Charlotte Christopherson
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What’s the best IP for machine learning workloads – CPU, GPU or NPU?

An outline of different machine learning use cases on devices and what workloads take place on the CPU, GPU and NPU

2 months ago by Sylwester Bala

Community Forums

Answered [ARM NN on Raspberry PI] libarmnn.so: undefined reference to `powf@GLIBC_2.27'
  • Raspberry Pi
  • Neural Network
  • TensorFlow
 0 votes 3565 views 7 replies Latest 1 months ago by aggado7 Answer this
Answered how to add api to arm
  • api
 0 votes 1843 views 3 replies Latest 3 months ago by Evano Answer this
Answered Save tensorflow model for ArmNN
  • Machine Learning (ML)
  • TensorFlow
  • Arm NN
 0 votes 1700 views 3 replies Latest 4 months ago by GeraldK Answer this
Answered ARM NN with ONNX Parser
  • Neural Network
  • Compilers
 0 votes 1586 views 2 replies Latest 6 months ago by Anto85 Answer this
Answered ARM NN build failed when linking libarm_compute_core-static.a
  • Cortex-A53
  • Arm NN
  • Arm Compute Library (ACL)
  • SIMD and Vector Execution
 0 votes 2512 views 3 replies Latest 1 years ago by Jason Andrews Answer this
Answered Matrix Multiplication Example in Arm Compute Library
  • Arm Compute Library (ACL)
 0 votes 5095 views 3 replies Latest 1 years ago by cdicker Answer this
Answered [ARM NN on Raspberry PI] libarmnn.so: undefined reference to `powf@GLIBC_2.27' Latest 1 months ago by aggado7 7 replies 3565 views
Answered how to add api to arm Latest 3 months ago by Evano 3 replies 1843 views
Answered Save tensorflow model for ArmNN Latest 4 months ago by GeraldK 3 replies 1700 views
Answered ARM NN with ONNX Parser Latest 6 months ago by Anto85 2 replies 1586 views
Answered ARM NN build failed when linking libarm_compute_core-static.a Latest 1 years ago by Jason Andrews 3 replies 2512 views
Answered Matrix Multiplication Example in Arm Compute Library Latest 1 years ago by cdicker 3 replies 5095 views