The FPGA Mezzanine Card standard (VITA 57.1) has significantly enhanced the FPGA ecosystem by decoupling the FPGA board from the input/output components. Years ago, if you wanted to process samples from an ADC, you would buy an FPGA board with an on-board ADC. If you wanted to add a DAC, or upgrade the ADC, you would replace the entire board! The FMC innovation provided developers with choice, so that they could select the right FPGA for their specific I/O requirements.
[Read More]
High-capacity non-volatile storage is pretty handy in the intensive computing applications that the Versal ACAP adaptive SoCs get employed in. NVMe SSDs are a perfect way to provide that storage because they can directly interface with the Versal’s integrated blocks for PCIe. Those integrated blocks are Gen4 compliant which makes for an extremely high bandwidth connection between the FPGA fabric and the storage medium.
Over the past couple of weeks, my team and I have been bringing up an NVMe SSD on the Versal AI Core VCK190 Evaluation kit using the FPGA Drive FMC Gen4 adapter.
[Read More]
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]
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]
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]
If you want to develop smart vision applications using Raspberry Pi cameras, the Zynq UltraScale+ MPSoC and Ubuntu 22.04 LTS, we’ve just released some code and the hardware to help you get started. For the launch of our new RPi Camera FMC, we’ve ported the Kria Vitis Platforms and Overlays over to the ZCU106 armed with an RPi Camera FMC. The result is that you can now build and develop the Smartcam and NLP-Smartvision applications in Certified Ubuntu 22.
[Read More]
FPGAs and MPSoCs are ideally suited for machine vision applications due to their ability to process large amounts of data in parallel and at high speeds. FPGAs can run highly power efficient neural network implementations and benefit from ultra low latency connections to multiple image sensors. Given the inherent strengths of FPGAs for machine vision, it surprises me that GPUs have become the dominant hardware platform for deep learning applications1 in recent years.
[Read More]
In the coming weeks we’ll be launching a new product called the RPi Camera FMC. This compact little FMC card allows you to connect 4x Raspberry Pi cameras (and variants) to a few Zynq UltraScale+ MPSoC boards listed below. All of the boards will support 4 cameras through the same FMC card.
AMD Xilinx ZCU104 AMD Xilinx ZCU102 AMD Xilinx ZCU106 TUL PYNQ-ZU Digilent Genesys-ZU Avnet UltraZed-EV Starter Kit I like Raspberry Pi and especially the impressive camera ecosystem that they and the RPi community have built up.
[Read More]
One of our 96B Quad Ethernet Mezzanine customers
has built an extremely tough looking heatsink to passively cool the
Ultra96
single board computer and it’s Ethernet mezzanine. I just wanted to share the
photos; it makes the Ultra96 look like a tank!
