DeepPoint-V2 Project

This repository contains the second version of the code base for Xilinx SDAccel FPGA implementation of Dynamic Graph CNN. The host program in this repository implements the out-of-order kernel queuing. The version with in-order queuing is available here. The paper could be accessed here.

Quick Refs

Basics:

• Building The Project
• Sample Data and Datasets
• Analyzing Profiled Runs
• Deploying on AWS-F1
• Results

Misc:

• Debugging The Project
• DDR Bank Assignment
• Tips & Tricks!

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 (>3.10, Do **not** use the 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).

Platforms

Refer to the table below.

Name	Supported Platform	Implementation	Datasets
CModel1	Cpu, Xil	CImplementationCpu, CImplementationXil	ShapeNet, ModelNet

Tests

There are two types of tests for the project, KernelTests and OclTests.

Kernel Tests

KernelTests are located at test/kerneltests and mainly aim for testing the HLS code of the kernels. To run the kernel tests, run:

make test

Ocl Tests

These tests use Google Test Framework to test the correctness of the kernel outputs in HW-EMU and HW modes along with testing all the OpenCL related infrastructure of the DeepPointFPGA. The main executable of OclTests is located at test/ocltests/. To execute the OclTests run this at the build directory:

sh LaunchOclTests.sh

Project Structure

Classes

• Tensors

  • CTensor<T> : CTensorBase
  • CTensorXil<T> : CTensorBase

• Implementations

  • CPlatformSelection
  • CImplementationCpu : CImplementationBase
  • CImplementationXil : CImplementationBase

• Kernels

  • CKernelWrapperBasicOps : CKernelWrapper
  • CKernelWrapperConcat : CKernelWrapper
  • CKernelWrapperConv : CKernelWrapper
  • CKernelWrapperGather : CKernelWrapper
  • CKernelWrapperMatmul : CKernelWrapper
  • CKernelWrapperPadUnpad : CKernelWrapper
  • CKernelWrapperReduce : CKernelWrapper
  • CKernelWrapperReluSqrtSquare : CKernelWrapper
  • CKernelWrapperTile : CKernelWrapper
  • CKernelWrapperTopK : CKernelWrapper
  • CKernelWrapperTranspose : CKernelWrapper

• Models

  • CClassifierMultiPlatform
  • CModel1

• Misc

  • CWeightLoader
  • CProfiler
  • CXilInfo
  • CStringFormatter

Branches

This repository contains multiple branches as described below:

Branch	AXI Width	DType	Tool	Notes
master	512-bits	float32	SDx2019.1	-

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}
}

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
DeepPointV1-FPGA	Our FPGA 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
rapidjson	A fast JSON parser/generator for C++ with both SAX/DOM style API	MIT
hls_tutorial_examples	(Paper) HLS examples and tutorials (Workshop)	BSD 3-Clause
SimplePasteBin	Python Library for working with PasteBin.com	GPL-3.0
googletest	GoogleTest - Google Testing and Mocking Framework	BSD 3-Clause