Hi Matteo,
Thanks for your contribution last week and sharing this design document with us. We'll have a look at it and get back to you with some feedback.
Sparse tensors support aligns well with the roadmap for ACL and we would be happy to review and take in your patch implementing this feature.
Hope this helps
________________________________
From: Matteo Nicoli <matteo.nicoli001(a)gmail.com>
Sent: 30 June 2025 17:33
To: Gunes Bayir <Gunes.Bayir(a)arm.com>; Pablo Tello <Pablo.Tello(a)arm.com>
Cc: acl-dev(a)lists.linaro.org <acl-dev(a)lists.linaro.org>
Subject: A draft for sparse tensors design
Dear Gunes and Pablo,
I noticed in the comments of the issue #1084<https://github.com/ARM-software/ComputeLibrary/issues/1084> that someone discussed the introduction of sparse tensors a while back. Although the last comment in the thread states, “There is currently no planned work to implement this feature. I’ll discuss it again with the team,” I thought the issue hadn’t been closed, indicating some interest in it. On Saturday, I began thinking on how this feature could be integrated into the codebase. I’ve drafted a design for these classes, which I’d like to share with you.
I understand that this feature may not be a priority for your team at the moment. However, if it doesn’t contradict your roadmap, once issue #1169<https://github.com/ARM-software/ComputeLibrary/issues/1169> is resolved, I’ll be able to begin implementing it myself on an experimental basis.
Best regards,
Matteo
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Hi everybody,
On Friday I opened a merge request on Gerrit with the implementation of the F32 dequantized output support for `NEConvolutionLayer` and `NEGEMMConvolutionLayer` (issue #1169 on GitHub). While I wait for a review, should I add a reference of the merge request on the GitHub issue? Or vice-versa, should I mention the GitHub issue in a merge request’s comment?
Best regards,
Matteo
Hi everybody,
I'm new to this mailing list. I signed up because I would like to start contributing to ComputeLibrary, but I can't find a TODO list anywhere (except for the 6 issues I see in the GitHub repo). I can start by checking if any of those 6 are not assigned to anyone.
Anyway, does anyone know if somewhere there is a roadmap or a list of features that you would like to implement?
Best regards,
Matteo
Hello,
The 25.03.1 release of Compute Library is out and comes with a
collection of improvements and new features.
Source code and prebuilt binaries are available at:
[1]https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.03.1
Highlights of the release:
* Add experimental QNX(R) support.
* Add matmul fp16->fp32 kernels to enable fp16 PyTorch attention
through ACL.
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the information in any medium. Thank you.
References
1. https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.03.1
Hello,
The 25.03 release of Compute Library is out and comes with a collection of improvements and new features.
Source code and prebuilt binaries are available at: https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.03
Highlights of the release:
* Notice: Migration to Semantic Versioning will take place by the end of April
* Modernize ACL CMake build
* Add a wrapper class for CpuPRelu operators
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Hello,
We’ve realized that the link to the documentation in our Compute Library v25.02.1 release announcement isn’t working at the moment. We’re sorry for the trouble!
Our team is actively working on getting this resolved as soon as possible. We’ll send an update once the link is accessible again.
Thanks for your patience, and let us know if you have any questions.
Best regards,
Yevgen
IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.
Hello,
The 25.02.1 release of Compute Library is out and comes with a
collection of improvements and new features.
Source code and prebuilt binaries are available at:
[1]https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.02.1
Highlights of the release:
* Add stateless support for GEMM kernels that need working_space
IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy
the information in any medium. Thank you.
References
1. https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.02.1
Hello,
The 25.02 release of Compute Library is out and comes with a collection of improvements and new features.
Source code and prebuilt binaries are available at: https://github.com/ARM-software/ComputeLibrary/releases/tag/v25.02
Highlights of the release:
* Detect number of CPU cores in OpenBSD
* Support tensors with dynamic shapes in NEGEMM
* Support FP16 dequantization in NEGEMMLowpMatrixMultiplyCore
* Add a public API for CpuMeanStdDevNormalization
* Enable BF16 inputs in CpuFullyConnected
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IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.
Dear ACL Development Team,
I hope this email finds you well. I am following up on my previous email
regarding the implementation of real-time inference using Arm Compute
Library.
To recap, I have successfully implemented inference for single images with
the object detection model on ARM devices, but I am looking to extend this
to real-time inference by feeding frames directly from a camera. From the
documentation I have reviewed, it seems that ACL supports input files in
formats like npy, jpg, and ppm.Could you please let me know if there is a
recommended approach or any existing functionality in ACL to achieve this?
In case there is no built-in support, I am currently exploring the ACL
source code to understand how this might be implemented. If possible, I
would appreciate any advice or suggestions on how to approach this, or any
resources that could assist in my efforts.
Thank you for your time and support. I look forward to hearing from you.
Best regards,
Darshan B Y
Dear ACL Development Team,
I am currently working on performing inference on ARM devices using an
object detection model with the Arm Compute Library (ACL). I have
successfully implemented inference for single images, obtaining correct
detections.
From the ACL documentation and examples I have reviewed, it appears the
library only supports input files in formats like npy, jpg, and ppm. I am
looking to implement real-time inference by feeding frames directly from a
camera. Could you please let me know if there is a recommended approach or
any existing functionality in ACL to achieve this?
Additionally, I have been exploring the ACL source code and am very
interested in working further with it. Any guidance or resources you could
provide would be greatly appreciated.
Thank you for your time and support.
Best regards,
Darshan B Y
