Visit Xilinx at CVPR 2018 to learn about quantized neural networks on embedded devices.
The following showcases and demos will be featured throughout the show in the Xilinx booth # 501.
Robotic car controlled by Xilinx PYNQ board demonstrates CNN and OpenCV acceleration for automotive.
Quad-Camera Machine Learning
High-performance CNN recognition on four live cameras simultaneously with a single Xilinx device.
Heterogeneous Sensor Fusion
Computer vision hardware acceleration of stereo depth, optical flow and CNN.
Cloud Surveillance with ML Analytics
Enabling better cloud video analytics with integrated transcoding and Machine Learning Inference on Amazon EC2 F1 instances powered by Xilinx FPGAs.
A realtime, low-latency, low-power object detection system using PYNQ running on a Zynq® UltraScale+™ MPSoC. CNN is based on the Darknet reference network.
A subset of Xilinx’s xfOpenCV libraries are exposed at the Python level using PYNQ. OpenCV components are accelerated in Programmable Logic.
Xilinx is hosting a hands-on developer lab at CVPR.
Machine Learning Developer Lab
Thursday, June 21 | 8:30 AM – 11:30 AM
Location: Salt Lake City Marriott Downtown at City Creek (located across the street from the Salt Palace Convention Center)
During this hands-on lab, data scientists and developers will work through a guided tutorial using the Xilinx ML Suite to deploy models for inference on Amazon EC2 F1 FPGA instances. This lab will cover how to use Python APIs for deploying inference models, and how to compile and quantize custom models.
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
Wednesday, June 20 | 10:10 AM – 12:30 PM
By Julian Faraone (University of Sydney); Nicholas Fraser (Xilinx); Michaela Blott (Xilinx); Philip H.W. Leong