Specifically, Dimitrios Danopoulos received the first price on the compute acceleration category in Xilinx OpenHW 2021 competition. In this project, they implemented an image reconstruction algorithm with Deep Learning, specifically with GANs (Generative Adversarial Networks). They trained and accelerated a Generator model in a Cloud FPGA (Alveo U50 FPGA) which was capable of reconstructing images of clothing with high speed and power efficiency.

The work has been done under the project “CloudAccel: Hardware Acceleration of Machine Learning Applications in the Cloud” that has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the Genal Secretariat for Research and Technology (GSRT) under grant agreement no 2212 and Xilinx University Program.

It’s worth mentioning that for this award in the competition, Xilinx will donate a high-performance FPGA (Alveo family) to Microprocessors Lab.

The project is available on GitHub:

https://github.com/cloudaccel/gan-hls

A video showing the main novelties is shown here:

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