ARCHIVED REPO

This is repo contains the in-order host program along with the FPGA implementation of DGCNN. Please use the new out-of-order version of the implementation available here. The latest changes and optimazations are only available on the out-of-order implementation's repository.

DeepPoint-V1 Project

This repository contains the code base for Xilinx SDAccel FPGA implementation of Dynamic Graph CNN model.

Build System

As easy as it is to use SDx GUI, it is recommended to use provided cmake scripts to run synthesis and build the binaries for both the selected FPGA platform and the host.

Dependencies

This project relies on these software/libraries(These should be installed on the OS):

Xilinx SDAccel 2019.1(Tested), 2018.3 2018.2 2017.4(Not Tested)
Xilinx XRT
python3(Symlinked as `python3`)
CMake3 (>3.0, Do **not** use default CMake package available on AWS-F1)
Bash (>4.0, Dash and others are not tested)
devtoolset-7 (>7.0, For C++14 support)

• Make sure that the latest vivado patches are applied, such as AR73068.

Configuration

To make it easier to explore the design space and try different configurations, all of the parameters that affect the output performance of the task kernels are gathered in a separate submodule repository at directory config. Also please note that various vivado directives for different steps are used to facilitate design implementation (opt, place, and route).

1. Building The Host Program

mkdir build
cd build

For CentOS 7.x, enable devtoolset-7 for C++14 support with:

scl enable devtoolset-7 bash

and continue with:

cmake ..
make DeepPointV1FPGA

In order to automate the building modes, an script for PasteBin has been developed to automatically upload the log files generated by XOCC during the compilation and the linking processes to PasteBin.com. Just make sure that it is enabled in the main CMakeLists.txt and username, password, and API key of your PasteBin account are set.
The linking process requires a large amount of free memory(~30GB of ram for 8 parallel jobs) and close to 5GBs of disk space which takes almost 14 hours to complete with a i7-6700HQ machine.

2. Compiling FPGA Kernels

Considering that step one is already done and current directory is build. This step generates *.xo files needed for the linking process.
For SW-Emulation:

make compile_swemu

For HW-Emulation:

make compile_hwemu

For HW(system build for real FPGA):

make compile_hw

3. Linking FPGA Kernels

Considering that steps one and two are already done and current directory is build. This step generates requested *.xclbin file needed for the host program.
For SW-Emulation:

make link_swemu

For HW-Emulation:

make link_hwemu

For HW(system build for real FPGA):

make link_hw

4. Automated Build

The autobuild scripts are intended to make building process on an AWS instance easier. They compile and link the project consecutively and take log of each step in a text file. Finally, after finishing up, the instance would be powered off with the sudo poweroff command. (considering that the root user has no password)
For SW-Emulation:

bash autobuild_swemu

For HW-Emulation:

bash autobuild_hwemu

For HW(system build for real FPGA):

bash autobuild_hw

5. Launching The Host Program

Considering that steps one, two and three are already done, current directory is build and the default shell is bash. This command is the unified solution to launch the host program in sw-emu, hw-emu or hw modes.

sh LaunchDeepPointV1FPGA.sh

The launcher script forwards its arguments to the host program.

Platforms

Refer to the table below.

Name	Supported Platform	Implementation	Notes
ModelArch01	CPU	CPU	CPU Only
ModelArch02	CPU, FPGA	Xilinx SDAccel Platform	FPGA Only

OpenCL UnitTests

To run the OCL unittests:

sh LaunchDeepPointV1FPGA.sh -t

Kernel-specific UnitTests

In order to make debugging of the kernels easier, separate unit tests are developed(test directory). These tests are isolated from OpenCL platform and therefore could be debugged as normal CPU c++ codes.

make test

6. AWS F1 Deployment

Please refer to AWS-F1-Wiki.md.

7. Project Structure

Branches

This repository contains multiple branches as described below:

Branch	AXI Width	DType	Tool	Notes
master	512-bits	float32	SDx2019.1	DEPRECATED
axi32	32-bits	float32	SDx2019.1	DEPRECATED
new_transpose	512-bits	float32	SDx2019.1	DEPRECATED
optimizing01	512-bits	float32	SDx2019.1	DEPRECATED
optimizing01_area_f1	512-bits	float32	SDx2019.1	Up-to-date
vitis20192_axi512	512-bits	float32	Vitis2019.2	HW build fails with clock partitioning error

8. Useful Tips

TCL scripts

There are two TCL scripts named PreRoute.tcl and PostRoute.tcl.

• The pre-route script is reponsible for generating the placed design Pre_route_checkpoint.dcp file.
• The post-route script generates SLR and per-block utilization and power estimation reports along with the routed design Post_route_checkpoint.dcp file.

Please note that Vivado could be used to open the design checkpoint files (*.dcp) to further explore the design implementation space. Open Vivado and choose File: Checkpoint: Open menu to import the desired checkpoint file.

The power estimation reports are generated in two formats (*.txt and *.rpx). The interactive report file (*.rpx) could be opened using the following Vivado TCL command: open_report -file <path to *.rpx> -name myreport.

Debugging Host-side in CLion

In order to debug the host-side program in any modes(sw_emu, hw_emu, or system), CLion or any other C++ IDE could be used.

Remember to run scripts/debug_script.sh before starting debugging session. Note that class XilinxImplementation is configured to select sw_emu in the case that variable XCL_EMULATION_MODE was not set beforehand.

Launching Vivado HLS

It is possible to launch Vivado HLS GUI and optimize the kernel of choice. This could be done after running a hw_emu build:

cd _x/task_<KERNEL>_solution/task_<KERNEL>
vivado_hls -p task_<KERNEL>

• Please note that any changes to the source files will be reflected on the main repository files.

9. Citation

Please use the following BibTeX entry:

@article{jamali2022dgcnn,
  title={DGCNN on FPGA: Acceleration of the Point Cloud Classifier Using FPGAs},
  author={Jamali Golzar, Saleh and Karimian, Ghader and Shoaran, Maryam and Fattahi Sani, Mohammad},
  journal={Circuits, Systems, and Signal Processing},
  pages={1--32},
  year={2022},
  publisher={Springer}
}

10. References

These repositories are used in this project:

Repo	Description	License
dgcnn	(Paper(ACM), Paper(Arxiv)) Dynamic Graph CNN for Point Clouds (Tensorflow)	N/S
DeepPointV1-GPGPU	Our CUDA/OCL Version of DGCNN	N/S
hlslib	(Paper) CMake/HLS Libraries for Intel and Xilinx	BSD 3-Clause
gemm_hls	(Paper(ACM), Paper(Arxiv)) Scalable matrix matrix multiplication on FPGA	BSD 3-Clause
pp4fpgas	(Book(Arxiv)) Parallel Programming for FPGAs	N/S
cnpy	C++ Library for working with *.npy files	MIT
PointNet	(Paper) PointNet 1	MIT
PointNet++	(Paper) PointNet 2	MIT
argparse	C++ Library for handling arguments	Apache-2.0-with-LLVM-Exception or GPL-3.0
spdlog	C++ Library for fast logging	MIT
hls_tutorial_examples	(Paper) HLS examples and tutorials (Workshop)	BSD 3-Clause
SimplePasteBin	Python Library for working with PasteBin.com	GPL-3.0