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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the information in any medium. Thank you.
References
1. https://github.com/ARM-software/ComputeLibrary/releases/tag/v24.11.1
Hello,
The 24.11 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
Highlights of the release:
* Add SVE SoftmaxLayer kernel for BF16
* Provide stateless API for CpuGemmLowpMatrixMultiplyCore,
CpuQuantize, and DequantizationLayer
* Extend static quantization interface for both matmul and
convolution operations
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/v24.11
Hello,
The 24.09 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/v24.09
Highlights of the release:
* Provide a wrapper class to expose cpu::CpuSoftmaxGeneric
* Detect number of cores in Windows®
* Add Optimized SME kernel for QASYMM8_SIGNED elementwise addition
operation
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/v24.09
Hello,
The 24.08.1 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/v24.08.1
Highlights of the release:
* Change inheritance qualifiers of experimental Cpu operator
interface classes to public for cpu-wrappers.
* Mismatches in static quantization updated after configure tests
* CpuSoftmax configure ignores is_log on validation
* Linker errors in armv8.2a Windows® builds
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/v24.08.1
Hello,
The v24.05 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/v24.05
Highlights of the release:
- Add CLScatter operator for FP32/16, S32/16/8, U32/16/8 data types.
- Various fixes to enable FP16 kernels in armv8a multi_isa builds.
- Updated logic in the OpenMP scheduler to exclude LITTLE cores.
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 v24.02.1 release of Compute Library is out and comes with a collection of improvements.
Source code and prebuilt binaries are available at: https://github.com/ARM-software/ComputeLibrary/releases/tag/v24.02.1
Highlights of the release:
- Fix performance regression in fixed-format kernels
- Fix compile and runtime errors in arm_compute_validation for Windows on Arm(WoA)
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 v23.11 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/v23.11
[https://opengraph.githubassets.com/9c6e9733a1038ab714edff3a08a9589bd88d70f3…]<https://github.com/ARM-software/ComputeLibrary/releases/tag/v23.11>
Release v23.11 · ARM-software/ComputeLibrary<https://github.com/ARM-software/ComputeLibrary/releases/tag/v23.11>
Public major release Documentation (API, changelogs, build guide, contribution guide, errata, etc.) available here: https://arm-software.github.io/ComputeLibrary/v23.11github.com
Highlights of the release:
- New features
- Add support for input data type U64/S64 in CLCast and NECast.
- Add support for output data type S64 in NEArgMinMaxLayer and CLArgMinMaxLayer
- Port the following kernels in the experimental Dynamic Fusion interface to use the new Compute Kernel Writer interface:
- experimental::dynamic_fusion::GpuCkwResize
- experimental::dynamic_fusion::GpuCkwPool2d
- experimental::dynamic_fusion::GpuCkwDepthwiseConv2d
- experimental::dynamic_fusion::GpuCkwMatMul
- Add support for OpenCL™ comand buffer with mutable dispatch extension.
- Add support for Arm® Cortex®-A520 and Arm® Cortex®-R82.
- Add support for negative axis values and inverted axis values in arm_compute::NEReverse and arm_compute::CLReverse.
- Add new OpenCL™ kernels:
- opencl::kernels::ClMatMulLowpNativeMMULKernel support for QASYMM8 and QASYMM8_SIGNED, with batch support
- Performance optimizations:
- Optimize cpu::CpuReshape
- Optimize opencl::ClTranspose
- Optimize NEStackLayer
- Optimize CLReductionOperation.
- Optimize CLSoftmaxLayer.
- Optimize start-up time of NEConvolutionLayer for some input configurations where GeMM is selected as the convolution algorithm
- Reduce CPU Overhead by optimal flushing of CL kernels.
- Deprecate support for Bfloat16 in cpu::CpuCast.
- Support for U32 axis in arm_compute::NEReverse and arm_compute::CLReverse will be deprecated in 24.02.
- Remove legacy PostOps interface. PostOps was the experimental interface for kernel fusion and is replaced by the new Dynamic Fusion interface.
- Update OpenCL™ API headers to v2023.04.17.
Thanks
ACL
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.
