In summer 2020, Xilinx announced the first Xilinx Adaptive Computing Challenge, teaming with Hackster.io to challenge independent developers to create applications using the Vitis Unified Software Platform and Vitis AI on select Xilinx hardware platforms. The contestants were challenged to solve real word problems in innovative ways. Participants competed in three categories: Intelligent Video Analytics using the ZCU104 Evaluation Kit, Adaptable Compute Acceleration leveraging the Alveo U50 Acceleration Card, and Adaptive Intelligence of Things using the Avnet Ultra96-V2 Development Board.
Seventy global projects were submitted. Participants showcased their expertise through their innovative design solutions. From the acceleration of binary neural networks to solve reinforcement learning challenges to an FPGA-based system that monitors facemask use through AI and automatic fall detection and alert system for older adults, the creativity and thought process dedicated to the challenge was truly on display in each innovative design solution.
Although many projects were exceptional, the judges selected the top three project winners in each category, who received prizes up to $10,000 (USD).
Category 1: Adaptable Compute Acceleration
- 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.
- Covid4HPC – A fast and accurate solution for Covid detection by Dimitrios Danopoulos: Detecting Covid-19 from X-Ray images using CNNs on cloud 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.
Category 2: Intelligent Video Analytics
- Facemask Detector by Victor Altamirano: FPGA-based system that monitors facemask use through artificial intelligence, includes a thermometer and facemask dispenser.
- 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.
- Checkout So Easy – Real-time Smart Retail System For FPGA by Team MAAX: Deploy an object detection model on DPU to build a system that can show detected commodities in VCU decoded video or images from camera.
Category 3: Adaptive Intelligence of Things
- Quad96 by Ussama Zahid: Quadcopter control and pole balancing using Deep Reinforcement Learning and Hand Gestures on Ultra96
- 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.
- 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
Learn more about the winners and their projects at the Adaptive Computing Challenge page on the Xilinx Developer site.
We’re also excited to announce that the updated Developer site will incorporate a new feature for the developer community, the Xilinx Developer Program. The free program provides the necessary resources for developers to create successful applications on every Xilinx platform. Members have access to the latest Xilinx development tools, exclusive tutorials, training, capabilities to share personal technical insights and projects, and many more key benefits.
