The Intel Movidius Neural Compute Stick (NCS) is a neural network computation engine in a USB stick form factor. It’s based on the Myriad-2 chip, referred to by Movidius as a VPU or Visual Processing Unit, basically a processor that was specifically designed to accelerate neural network computations, and with relatively low power requirements. The NCS is a great match for single board computers like the Raspberry Pi, the Beagle Bone and especially the PYNQ-Z1.
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PYNQ enables huge productivity gains by making it possible to program the Zynq-7000 SoC with a high-level programming language (Python) and leverage the power of FPGA hardware acceleration with ease. Xilinx first designed PYNQ to target the PYNQ-Z1 board but it wasn’t long before others saw the potential of running PYNQ on other platforms. This post is a list of open-sourced PYNQ projects and ports that run on other platforms. I’ll keep the list up-to-date but if you know of an open-sourced PYNQ project or port that I haven’t found yet, please let me know and I’ll add it to the list.
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See what the PYNQ-Z1 and the PYNQ Computer Vision overlay are capable of doing with a 720p standard HD video stream. In the video we run a 2D filter and dilate function on the incoming video, first using the Python OpenCV functions (ie. without hardware acceleration), then we test it again with the accelerator IPs running on the FPGA. Without acceleration, we get a frame rate of 5 frames per second.
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This video demonstrates how you would typically go about accelerating a Python function or algorithm on the Zynq-7000 with PYNQ. The function I chose to base this video on is the Finite Impulse Response (FIR) filter because the SciPy package contains the lfilter function which can be used for this purpose, and because the Xilinx IP catalog has a free FIR filter IP core. If you instead wanted to implement the accelerator in HLS, the process would be very similar, you would just have to design your accelerator with AXI-Streaming interfaces and ensure that the TLAST signals were properly managed.
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Avnet Silica was at Embedded World 2018 in Nuremburg, Germany last February demonstrating some cool industrial networking solutions such as TSN and FOSS GNU/Linux security concepts on Opsero’s Robust Ethernet FMC and the Zynq UltraScale+. Get a glimpse of the hardware at 0:54. If you saw the demo in person, I’d love to know what you thought of it.
In this video tutorial we create a custom PYNQ overlay for the PYNQ-Z1 board. Probably the simplest PYNQ overlay possible, it contains one custom IP (an adder) with an AXI-Lite interface and three registers accessible over that interface: a, b and c. To use the IP we write a number to input registers a and b, and then we read the output register c which contains the sum of a and b.
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Being a big fan of Python, for ages I’ve wanted to explore the possibilities of running Python on the Zynq. Thankfully Xilinx and Digilent saw the value in this too and they developed the PYNQ-Z1 and more importantly the PYNQ libraries for Python. The PYNQ-Z1 is basically a single board computer based on the Zynq-7020 device from Xilinx. So thats got a dual core ARM plus integrated FPGA or programmable logic.
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Early this year IntelliProp released a demo video of their NVMe Host Accelerator IP core running on the Intel Arria 10 GX FPGA Development board. As you can see in the video, they are using Opsero’s FPGA Drive product with the PCIe slot connector to interface the NVMe SSD to the FPGA board. They measured an impressive performance of around 2300MBps sequential write speed and 3200MBps sequential read speed.
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In the final part of the Arty base project tutorial, we build a PetaLinux project that’s tailored to our Arty base design. Then we boot PetaLinux on our hardware and verify that we have network connectivity by checking the Arty’s DHCP assigned IP address and then pinging it from a PC.
Tools used I used the following setup to do this project:
Vivado 2017.3 on a Windows 10 machine PetaLinux 2017.
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Here’s a base project for the Arty board based on the Artix-7 FPGA. The Arty is a nice little dev board because it’s low cost ($99 USD) but it’s still got enough power and connectivity to make it very useful. I really like the fact that the JTAG and UART are both accessed through the same USB connector, so I only need to connect one USB cable.
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