🚫 This repository has been archived. For up-to-date ResNet50 dataflow FPGA acceleration, please see FINN Examples.
Quantized ResNet50 Dataflow Acceleration on Alveo
This repository contains an implementations of a binary ResNet50 FINN-style dataflow accelerator targeting Alveo boards.
It is intended as a showcase of achievable throughput and latency for ImageNet clasiffication on FPGA, using dataflow execution and on-chip weight storage.
Repo organization
The repository is organized as follows:
- src: contains source code and submodules
- hls: HLS custom building blocks and submodules to FINN librares (FINN and FINN-HLSLib)
- w1a2-v1.0: pre-build weights, thresholds, directives and configuration files for Binary ResNet50
- compile: contains scripts for accelerator compilation (Vivado HLS CSynth + Vivado Synthesis)
- link: contains scripts for accelerator linking into the Alveo platform with Vitis
- host: python and Jupyter host code, using PYNQ for Alveo
Building the Accelerator
The Accelerator is built using Vitis 2019.2. We recommend using this version,
otherwise changes might be required to source and/or Makefiles for things to
work.
To build the accelerator, clone the repository (using --recursive to pull submodules), after which:
cd ResNet50-PYNQ/compile
make NET=w1a2_v1.0
cd ../link
make
See the specific Compile and Link documentation for further info.
Running the Demo
After you have built the accelerator, you can install the required files in
the host folder. Move in the cloned repo and do make install
cd ResNet50-PYNQ
make install
You can then run the included Jupyter notebook or the Python multithreaded inference example. If you
want to use the distributed PYNQ python package, please read below.
If you want to run example Python inference code, please see the host code documentation.
PYNQ quick start
Install the resnet50-pynq package using pip:
pip install resnet50-pynq
After the package is installed, to get your own copy of the available notebooks
run:
pynq get-notebooks ResNet50
You can then try things out by doing:
cd pynq-notebooks
jupyter notebook
There are a number of additional options for the pynq get-notebooks command,
you can list them by typing
pynq get-notebooks --help
You can also refer to the official
PYNQ documentation for more information
regarding the PYNQ Command Line Interface and in particular the
get-notebooks command.
Supported Boards/Shells
Currently, we distribute the overlay only for the following Alveo boards and
shells:
| Shell |
Board |
| xilinx_u250_xdma_201830_2 |
Xilinx Alveo U250 |
Designs are built using Vitis 2019.2.
Author
Lucian Petrica @ Xilinx Research Labs.
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