Adaptive Computing Challenge 2020 Winning Projects

In summer 2020, we kicked off the first-ever Xilinx Adaptive Computing Challenge! We teamed up with Hackster.io, the world's fastest-growing developer community for learning, programming, and building hardware, to challenge independent developers to use Vitis Unified Software PlatformVitis AI, and one Xilinx hardware platform to create a solution for the real word problems we face today.  The top three projects in each category received prizes up to $10,000 (USD)

Adaptable Compute Acceleration

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Use an Alveo™ U50 Accelerator Card, combined with Xilinx Vitis / Vitis AI to implement hardware acceleration of challenging workloads, including but not limited to financial computing, machine learning, computational storage, and data search and analytics. 

Intelligent Video Analytics

xilinx-global-competition-closed-division-zcu (4)

Use the Zynq® UltraScale+™ MPSoC ZCU104 Evaluation Kit combined with Xilinx Vitis / Vitis AI to build an intelligent video analytics solution, including but not limited to smart city, smart retail, ADAS, robotics vision, medical imaging and many other applications. 

Adaptable Compute Acceleration

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Use an Avnet Ultra96-V2 Development Board, combined with Xilinx Vitis / Vitis AI to build any application that shows the best use of hardware acceleration with programmable logic.


Winning Projects

Category 1: Adaptable Compute Acceleration

First Place

Acceleration of Binary Neural Networks using Xilinx FPGA

Acceleration of Binary Neural Networks using Xilinx FPGA

By Raul Valencia

Exploit Xilinx FPGA's hardware to train neuroevolved binary neural networks, then solve Reinforcement Learning problems (like Atari Games).

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Second Place

Covid4HPC

Covid4HPC - A Fast and Accurate Solution for Covid Detection

By Dimitrios Danopoulos

Detecting Covid-19 from X-Ray images using CNNs on cloud FPGAs
 

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Third Place

ThunderGP HLS-based Graph Processing Framework on FPGAs

ThunderGP: HLS-based Graph Processing Framework on FPGAs

By Xinyu Chen

ThunderGP enables data scientists to enjoy the performance of FPGA-based graph processing without compromising programmability.

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Category 2: Intelligent Video Analytics

First Place

facemask-detector

Facemask Detector

By Victor Altamirano

FPGA-based system that monitors facemask use through artificial intelligence, includes a thermometer and facemask dispenser.
 

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Second Place

Automatic fall detection for elderly people

Automatic Fall Detection for Elderly People

By Jinin K Jose, Nevil Shah, and Rohin Kumar

Human falls is a major reason for deaths in elderly people. It can be prevented by an automatic fall detection and alert system.
 

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Third Place

Checkout So Easy - Real-time Smart Retail System For FPGA

Checkout So Easy - Real-time Smart Retail System For FPGA

By Team MAAX

Deploy an object detection model on DPU to build a system which can show detected commodities in VCU decoded video or images from camera.

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Category 3: Adaptive Intelligence of Things

First Place

quad96

Quad96

By Ussama Zahid


Quadcopter control and pole balancing using Deep Reinforcement Learning and Hand Gestures on Ultra96
 

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Second Place

Hardware Accelerated Real-time Perception in 3D (HARP-3D)

Hardware Accelerated Real-time Perception in 3D (HARP-3D)

By Sambit Mohapatra

End-to-end demonstration of 3D object detection in LiDAR point clouds using a deep neural network running on the ULTRA96V2.

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Third Place

LAMP-FPGA Accelerating Time Series Similarity Prediction

LAMP-FPGA: Accelerating Time Series Similarity Prediction

By Amin Kalantar and Philip Brisk

Predicting similar patterns in time series data on Ultra96-V2 FPGA board
 

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