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
Hello,
The 24.11.1 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/v24.11.1
Highlights of the release:
* Add stateless GEMM execution via ICPPKernel::run_op
* TensorShape class supports dynamic shapes
* Add skeletons for Dynamic GEMM operator
* Convert Double rounding to Single rounding quantization behaviour
in both Cpu/Gpu backend
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References
1. https://github.com/ARM-software/ComputeLibrary/releases/tag/v24.11.1
