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)
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.
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.
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.
Exploit Xilinx FPGA's hardware to train neuroevolved binary neural networks, then solve Reinforcement Learning problems (like Atari Games).View Project
Detecting Covid-19 from X-Ray images using CNNs on cloud FPGAs
ThunderGP enables data scientists to enjoy the performance of FPGA-based graph processing without compromising programmability.View Project
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 Project
End-to-end demonstration of 3D object detection in LiDAR point clouds using a deep neural network running on the ULTRA96V2.View Project
Predicting similar patterns in time series data on Ultra96-V2 FPGA board