Tiny YOLO v3 ZYNQ

What is this project about?

FPGA implementation of YOLOv3-tiny

• Scalable & parameterizable

• Latency-driven

• Tailored for FPGA device with limited resources

Latency and resource analytical models

• Hardware and software latency
• DSP and BRAM utilization

Design Space Exploration to identify the Pareto-optimal design point on Zedboard

To cite our work

Our paper is accepted by ARC2020 (https://arcoresearch.com/arc2020/)

@inproceedings{yu2020parameterisable, title={A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny}, author={Yu, Zhewen and Bouganis, Christos-Savvas}, booktitle={Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2020. Lecture Notes in Computer Science, vol 12083}, pages={330-344}, year={2020}, month={03}, publisher={Springer, Cham}, url={https://doi.org/10.1007/978-3-030-44534-8_25} }

Navigate inside the project

/code

main codebase including "templates" (managed by the script) and a design example (with bitfile and sdk, ready for deployment on Zedboard)

/data

weights and test data

/document

include the paper (recommend read first) and thesis (more detailed)

/model

code used for analytical models and design space exploration

/scripts

entry point for the automated framework

/tools

some tools used for helping the test, not important

How to use the automated flow

Check environment

• ubuntu 16.04 LTS
• Vivado v2019.1
• python 3.5.2
• gcc 5.4.0

Set target FPGA, clock and resource constraints by

Edit scripts/run_all.py

Currently, the following FPGA (on Zedboard) has been tested. But the design should work for other Xilinx Zynq devices

device = "xc7z020-clg484-1"
clk_ns = "10"

Edit model/main.cpp

You have to specify resources constriants. The script is not able to infer resources available from the device you previously chose.

#define DEFAULT_MAX_DSP (220) // zynq7020
#define DEFAULT_MAX_BRAM_18k (280) // zynq7020
#define DEFAULT_MAX_ULTILISATION (0.9)  // usually won't use 100% resources

Run scripts/run_all.py

2000 years later...

You will have the Vivado SDK GUI

Create an application project, add files from code/sdk (Notice if you have changed the resource constraints, you need to manually update the folding factors in make_layer_group)

Increase the heap size as code/sdk/src/lscript.ld shows

The latency of the inference shall be printed which indicates the system is working

If you want to get the bounding boxes, please refer to https://github.com/pjreddie/darknet for more details on converting the network outputs to bbox

Feed different images

Unfortunately, the current system works as bare-metal without an OS and the system does not include a camera interface. Therefore, if you wish to feed different images into the network, you have to do it at compile-time by converting it to a header file.

• Load an image and convert it to an array of 3*416*416 by tools/image_load
• Quantise the image by tools/head_short
• Pad the array to the size of 4*416*416 with tools/input_channel_pad
• Replace code/sdk/include/group_0_input.h with the generated header file

Meanwhile, if you wish to verify the network output, a fixed point YOLO implementation which can run on the desktop has been provided as the reference. https://github.com/Yu-Zhewen/Tiny_YOLO_v3_ZYNQ/tree/1c629ad5592a63be0ec61391265feefe9e58068b/sw/desktop_fp_ref

Please do the following steps:

• Run the reference project with your image, and you shall obtain a dat file as the reference output.
• Use tools/dat_fp_to_short to convert the datatype to short
• Use tools/dat_to_head to convert the dat file into a header file
• Pad the reference output from 26*26*255 to 26*26*256, by tools/yolo_pad
• Transpose the output by tools/interleave_output_group
• Replace code/sdk/include/group_13_output.h with the generated header file

Contact me

you can either create an issue or just drop me an email ([email protected])