Zynq UltraScale+ XCZU7EV-2FFVC1156 MPSoC
The onboard System Controller reads FRU data on the EEPROM of the connected FMC card, and sets the VADJ voltage accordingly.
Boot mode is determined by DIP switch SW6.
In this post we’re going to build a smart camera using a Raspberry Pi camera, the ZCU104 board and PetaLinux. We’re going to do this by leveraging the Smartcam app which was originally designed for the AMD Xilinx Kria SoM. Our version of the Smartcam app can take video inputs from a Raspberry Pi camera, a USB camera or a file, and it can output video to a DisplayPort monitor, a file or via Ethernet over Real-time Transport Protocol. [Read More]
Benchmarking an FPGA based AI Vision application
Docker, Ubuntu and PetaLinux put to the test
Many smart vision applications need to make fast decisions: autonomous vehicles, drones, surveillance and industrial robotics are only a few examples. When developing these kinds of AI vision systems, understanding performance-affecting factors is critical. In this post, we’ll explore two such factors: the operating system and camera type. We’ll measure and compare the performance of the NLP-SmartVision app on the ZCU104 board. The setups we’ll use are: Operating system: Docker container on Certified Ubuntu 22. [Read More]
NLP-SmartVision in PetaLinux on ZCU104
Using Raspberry Pi cameras
In the last post we looked at how to run the Smartcam and NLP-SmartVision apps on the ZCU106 and Certified Ubuntu 22.04 LTS. One reader mentioned that running these apps in a Docker container on Ubuntu probably comes with a performance penalty when compared to running it on a lean PetaLinux build. This piqued my curiosity so in this post, we’re going to get the NLP-Smartvision app running in PetaLinux on the ZCU104 and then in the next post we’ll measure whatever penalty there may be to the throughput (in frames per second) and/or the glass-to-glass latency (in milliseconds). [Read More]