Pipe-All is an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs.
Details of Pipe-ALL can be found in our paper (pre-print), and please consider citing this paper in your work if you find this implementation useful in your research.
Ehsan Aghapour, Gayathri Ananthanarayanan, Anuj Pathania. "Integrated ARM big. Little-Mali Pipeline for High-Throughput CNN Inference." TechRxiv.
We create the pipe-all for AlexNet, GoogleNet, MobileNet, ResNet50 and SqueezeNet graphs. The pipe-all versions of these graphs are graph_AlexNet_all_pipe_sync, graph_GoogleNet_all_pipe_sync, graph_MobileNet_all_pipe_sync, graph_ResNet50_all_pipe_sync, and graph_SqueezeNet_all_pipe_sync respectively.
git clone https://github.com/Ehsan-aghapour/ARMCL-PipeALL.git -b pipe-all
After compiling the source code and preparing the libraries based on your platform run the following command:
./graph_AlexNet_all_pip_sync --threads=4 --threads2=2 --total_cores=6 --partition_point=3 --partition_point2=5 --order=G-L-B --n=60 --image=data_dir/images/ --data=data_dir/ --labels=data_dir/label.txt
--threads: Number of threads for Big cluster.
--threads2: Number of threads for little cluster.
--total_cores: Number of all cores of CPU.
--partition_point: The first partitioning point. First partitioning will happen after layer specified with this argument.
--partition_point2: The second partitioning point. second partitioning will happen after layer specified with this argument.
--order: The order of components in the pipeline. (G:GPU, B:CPU Big cluster, L:CPU Little cluster). For example G-B-L order means first subgraph runs in GPU, Second subgraph runs in CPU Big cluster and third subgraph runs in CPU little cluster.
--n: Number or runs. For example 60 means running graph for 60 frames.
The following image, data and lablels should specified if you want to run the graph for real data. But if you want to run the network for dummy data (random data and image) do not specify this arguments:
--image: dir which include image files. graph will run for images inside this dir.
--data: dir of graph parameters.
--labels: label file
The following parts explain compiling and running ARMCL for android and linux platforms.
First it is required to prepare cross compile tools to compile source code in linux system for android target. Here is the steps to download and settup tools.
1- Download Android NDK:
https://developer.android.com/ndk/downloads
2- We should create a standalone toolchains for compiling source code for android. Based on your platform set --arch to arm or arm64 in the following command. $corss-compile-dir is your arbitrary dir at which cross compile toolchains will be created.
$NDK/build/tools/make_standalone_toolchain.py --arch arm/arm64 --api 23 --stl gnustl --install-dir $cross_compile_dir
This command create cross compile toolchains at $cross-compile-dir.
3- Add $cross-compile-dir/bin to the path:
export PATH=$cross-compile-dir/bin/:$PATH
4- Go to the ARMCL source dir (cd $ARMCL-source-dir) and use the following command to compile it. Based on your platform set arch to armv7a or arm64-v8a in this command.
CXX=clang++ CC=clang scons Werror=0 debug=0 asserts=0 neon=1 opencl=1 os=android arch=armv7a/arm64-v8a -j8
For cross compiling the source code in linux host for linux host you require:
gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf for 32 bit target
gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu for 64 bit target
Then use the following command to compile. Based on your platform set arch to armv7a or arm64-v8a in this command.
scons Werror=0 -j16 debug=0 asserts=0 neon=1 opencl=1 os=linux arch=armv7a/arm64-v8a
For android it is required to specify the path of libOpenCL.so. First copy this library into an arbitrary dir ($lib_dir) and set LD_LIBRARY_PATH to this dir:
cp /system/lib64/egl/libGLES_mali.so $lib_dir/libOpenCL.so
export LD_LIBRARY_PATH=$lib_dir
Now it is ready to run built binaries in build dir of ARMCL.
For AlexNet there is a zip file of model paramters, sample images and a label file. So you can run this graph for real data and see the results. For this purpose first download this zip file at:
https://developer.arm.com/-/media/Arm%20Developer%20Community/Images/Tutorial%20Guide%20Diagrams%20and%20Screenshots/Machine%20Learning/Running%20AlexNet%20on%20Pi%20with%20Compute%20Library/compute_library_alexnet.zip?revision=c1a232fa-f328-451f-9bd6-250b83511e01&la=en&hash=7371AEC619F8192A9DE3E42FE6D9D18B5119E30C
make a directory and extract this zip file:
mkdir $assets_alexnet
unzip compute_library_alexnet.zip -d $assets_alexnet
Run the AlexNet graph with this command. Select NEON or CL to run it on CPU or GPU respectively:
./build/examples/graph_alexnet Neon/CL $assets_alexnet $assets_alexnet/go_kart.ppm $assets_alexnet/labels.txt
For linux in addition to libOpencL.so, these three libraries should be copied into target. So first copy these libraries from the ARMCL dir:
cp build/libarm_compute.so build/libarm_compute_core.so build/libarm_compute_graph.so $lib_dir
Then copy libOpenCL.so into $lib_dir and set LD_LIBRARY_PATH to them:
cp /system/lib64/egl/libGLES_mali.so $lib_dir/libOpenCL.so
export LD_LIBRARY_PATH=$lib_dir
Now it is ready to run built binaries in build dir of ARMCL.
For AlexNet there is a zip file of model paramters, sample images and a label file. So you can run this graph for real data and see the results. For this purpose first download this zip file at:
https://developer.arm.com/-/media/Arm%20Developer%20Community/Images/Tutorial%20Guide%20Diagrams%20and%20Screenshots/Machine%20Learning/Running%20AlexNet%20on%20Pi%20with%20Compute%20Library/compute_library_alexnet.zip?revision=c1a232fa-f328-451f-9bd6-250b83511e01&la=en&hash=7371AEC619F8192A9DE3E42FE6D9D18B5119E30C
make a directory and extract this zip file:
mkdir $assets_alexnet
unzip compute_library_alexnet.zip -d $assets_alexnet
Run the AlexNet graph with this command. Select NEON or CL to run it on CPU or GPU respectively:
./build/examples/graph_alexnet Neon/CL $assets_alexnet $assets_alexnet/go_kart.ppm $assets_alexnet/labels.txt
Release repository: https://github.com/arm-software/ComputeLibrary
Development repository: https://review.mlplatform.org/#/admin/projects/ml/ComputeLibrary
Please report issues here: https://github.com/ARM-software/ComputeLibrary/issues
Make sure you are using the latest version of the library before opening an issue. Thanks
News:
- 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
Related projects:
Tutorials:
- Tutorial: Cartoonifying Images on Raspberry Pi with the Compute Library
- Tutorial: Running AlexNet on Raspberry Pi with Compute Library
Documentation (API, changelogs, build guide, contribution guide, errata, etc.) available at https://github.com/ARM-software/ComputeLibrary/wiki/Documentation.
Binaries available at https://github.com/ARM-software/ComputeLibrary/releases.
-
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
- Android™
- Bare Metal
- Linux®
- macOS®
- Tizen™
The software is provided under MIT license. Contributions to this project are accepted under the same license.
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
Before the ComputeLibrary project 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.
Android is a trademark of Google LLC.
Arm, Cortex and Mali 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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent