Compute Library

The Compute Library is a collection of low-level machine learning functions optimized for Arm® Cortex®-A and Arm® Mali™ GPUs architectures.

The library provides superior performance to other open source alternatives and immediate support for new Arm® technologies e.g. SVE2.

Key Features:

• Open source software available under a permissive MIT license
• Over 100 machine learning functions for CPU and GPU
• Multiple convolution algorithms (GeMM, Winograd, FFT, Direct and indirect-GeMM)
• Support for multiple data types: FP32, FP16, INT8, UINT8, BFLOAT16
• Micro-architecture optimization for key ML primitives
• Highly configurable build options enabling lightweight binaries
• Advanced optimization techniques such as kernel fusion, Fast math enablement and texture utilization
• Device and workload specific tuning using OpenCL tuner and GeMM optimized heuristics

Documentation

Note: The documentation includes the reference API, changelogs, build guide, contribution guide, errata, etc.

Pre-built binaries

All the binaries can be downloaded from here or from the tables below.

Platform	Operating System	Release archive (Download)
Raspberry Pi 4	Linux 32bit
Raspberry Pi 4	Linux 64bit
Odroid N2	Linux 64bit
HiKey960	Linux 64bit

Architecture	Operating System	Release archive (Download)
armv7	Android
armv7	Linux
arm64-v8a	Android
arm64-v8a	Linux
arm64-v8.2-a	Android
arm64-v8.2-a	Linux

Supported Architectures/Technologies

• Arm® CPUs:

  • Arm® Cortex®-A processor family using Arm® Neon™ technology
  • Arm® Cortex®-R processor family with Armv8-R AArch64 architecture using Arm® Neon™ technology
  • Arm® Cortex®-X1 processor using Arm® Neon™ technology

• Arm® Mali™ GPUs:

  • Arm® Mali™-G processor family
  • Arm® Mali™-T processor family

• x86

Supported Systems

• Android™
• Bare Metal
• Linux®
• macOS®
• Tizen™

Resources

• Tutorial: Running AlexNet on Raspberry Pi with Compute Library
• Gian Marco's talk on Performance Analysis for Optimizing Embedded Deep Learning Inference Software
• Gian Marco's talk on optimizing CNNs with Winograd algorithms at the EVS
• Gian Marco's talk on using SGEMM and FFTs to Accelerate Deep Learning

How to contribute

Contributions to the Compute Library are more than welcome. If you are interested on contributing, please have a look at our how to contribute guidelines.

Developer Certificate of Origin (DCO)

Before the Compute Library accepts your contribution, you need to certify its origin and give us your permission. To manage this process we use the Developer Certificate of Origin (DCO) V1.1 (https://developercertificate.org/)

To indicate that you agree to the the terms of the DCO, you "sign off" your contribution by adding a line with your name and e-mail address to every git commit message:

Signed-off-by: John Doe <[email protected]>

You must use your real name, no pseudonyms or anonymous contributions are accepted.

Public mailing list

For technical discussion, the ComputeLibrary project has a public mailing list: [email protected] The list is open to anyone inside or outside of Arm to self subscribe. In order to subscribe, please visit the following website: https://lists.linaro.org/mailman/listinfo/acl-dev

License and Contributions

The software is provided under MIT license. Contributions to this project are accepted under the same license.

Trademarks and Copyrights

Android is a trademark of Google LLC.

Arm, Cortex, Mali and Neon are registered trademarks or trademarks of Arm Limited (or its subsidiaries) in the US and/or elsewhere.

Linux® is the registered trademark of Linus Torvalds in the U.S. and other countries.

Mac and macOS are trademarks of Apple Inc., registered in the U.S. and other countries.

Tizen is a registered trademark of The Linux Foundation.