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 Platform, Vitis 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
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
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
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
By Raul Valencia
Exploit Xilinx FPGA's hardware to train neuroevolved binary neural networks, then solve Reinforcement Learning problems (like Atari Games).
View ProjectSecond Place
Covid4HPC - A Fast and Accurate Solution for Covid Detection
By Dimitrios Danopoulos
Detecting Covid-19 from X-Ray images using CNNs on cloud FPGAs
Third Place
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.
View ProjectCategory 2: Intelligent Video Analytics
First Place
Facemask Detector
By Victor Altamirano
FPGA-based system that monitors facemask use through artificial intelligence, includes a thermometer and facemask dispenser.
Second Place
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.
Third Place
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.
View ProjectCategory 3: Adaptive Intelligence of Things
First Place
Quad96
By Ussama Zahid
Quadcopter control and pole balancing using Deep Reinforcement Learning and Hand Gestures on Ultra96
Second Place
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.
View ProjectThird Place
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
