Over on GitHub, [ttsiodras] wanted to learn VHDL. So he started with an algorithm to do Mandelbrot sets and moved it to an FPGA. Because of the speed, he was able to accomplish real-time zooming. You can see a video of the results, below.
The FPGA board is a ZestSC1 that has a relatively old Xilinx Spartan 3 chip onboard. Still, it is plenty powerful enough for a task like this.
If you want to use FPGAs, you’ll almost always use an HDL like Verilog or VHDL. These are layers of abstraction just like using, say, a C compiler is to machine language or assembly code. There are other challengers to the throne such as SpinalHDL which have small but enthusiastic followings. [Tom] has a post about how the VexRISC-V CPU leverages SpinalHDL to make an extremely flexible system that is as efficient as plain Verilog. He says the example really shows off why you should be using SpinaHDL.
Like a conventional programming language, it is easy to find niche languages that will attract a little attention and either take off (say, C++, Java, or Rust) or just sort of fade away. The problem is you can’t ever tell which ones are going to become major and which are just flashes in the pan. Is SpinalHDL the next big thing? We don’t know.
A few years back, a company by the name of Pano Logic launched a line of FPGA-based thin clients. Sadly, the market didn’t eventuate, and the majority of this stock ended up on eBay, to eventually be snapped up by eager hackers. [Tom] is one of those very hackers, and decided to try some raytracing experiments with the hardware.
[Tom] has one of the earlier Pano Logic clients, with VGA output and a Xilinx Spartan-3E 1600 FPGA under the hood. Due to limited RAM in the FPGA, and wanting to avoid coding a custom DRAM controller for the memory on the board, there just wasn’t room for a framebuffer. Instead, it was decided that the raytracer would instead “race the beam” – calculating each pixel on the fly, beating the monitor’s refresh rate.
This approach means that resource management is key, and [Tom] notes that even seemingly minor changes to the raytracing environment require inordinately large increases in calculation. Simply adding a shadow and directional light increased core logic utilisation from 66% to 92%!
While the project may not be scalable, [Tom] was able to implement the classic reflective sphere, which bounces upon a checkered plane and even added some camera motion to liven things up through an onboard CPU core. It’s a real nuts-and-bolts walkthrough of how to work with limited resources on an FPGA platform. Code is available on Github if you fancy taking a further peek under the hood.
If you’re new to FPGAs yourself, why not check out our FPGA bootcamp?
We reported earlier about Xilinx offering free-to-use ARM Cortex M1 and M3 cores. [Adam Taylor] posted his experiences getting things working and there’s also a video done by [Geek Til It Hertz] based on the material that you can see in the second video, below.
The post covers using the Arty A35T or Arty S50 FPGA boards (based on Artix FPGAs) and the Xilinx Vivado software. Although Vivado will allow you to do conventional FPGA development, it also can work to compose function blocks to produce CPUs and that’s really what’s going on here.
Continue reading “Getting Started With Free ARM Cores On Xilinx”
[Adam Taylor] always has interesting FPGA posts and his latest is no exception. He wanted to use a Zynq for image processing. Makes sense. You can do the high-speed parallel parts in the FPGA fabric and do higher-level processing on the built-in CPU. The problem is, of course, you need to get the video data into the system. [Adam] elected to use the Mobile Industry Processor Interface (MIPI) Camera Serial Interface Issue 2 (CSI-2).
This high-speed serial interface is optimized for data flowing in one direction. The camera, or the master, sends a number of bits (at least one) serially with one clock. To increase speed, data transfers on both rising and falling clock edges. The slave also has a pretty standard I2C master to send commands to the camera which, for the purposes of I2C, is the slave.
One of the ways people use FPGAs is to have part of the FPGA fabric hold a CPU. That makes sense because CPUs are good at some jobs that are hard to do with an FPGA, and vice versa. Now that the RISC-V architecture is available it makes sense that it can be used as an FPGA-based CPU. [Clifford Wolf] created PicoSOC — a RISC-V CPU made to work as a SOC or System on Chip with a Lattice 8K evaluation board. [Mattvenn] ported that over to a TinyFPGA board that also contains a Lattice FPGA and shows an example of interfacing it with a WS2812 intelligent LED peripheral. You can see a video about the project, below.
True to the open source nature of the RISC-V, the project uses the open source Icestorm toolchain which we’ve talked about many times before. [Matt] thoughtfully provided the firmware precompiled so you don’t have to install gcc for the RISC-V unless you want to write you own software. Which, of course, you will.
It’s the 21st century, and according to a lot of sci-fi movies we should have perfected AI by now, right? Well we are getting there, and this project from a group of Cornell University students titled, “FPGA kNN Recognition” is a graceful attempt at facial recognition.
For the uninitiated, the K-nearest neighbors or kNN Algorithm is a very simple classification algorithm that uses similarities between given sets of data and a data point being examined to predict where the said data point belongs. In this project, the authors use a camera to take an image and then save its histogram instead of the entire image. To train the network, the camera is made to take mug-shots of sorts and create a database of histograms that are tagged to be for the same face. This process is repeated for a number of faces and this is shown as a relatively quick process in the accompanying video.
The process of classification or ‘guess who’, takes an image from the camera and compares it with all the faces already stored. The system selects the one with the highest similarity and the results claimed are pretty fantastic, though that is not the brilliant part. The implementation is done using an FPGA which means that the whole process has been pipe-lined to reduce computational time. This makes the project worth a look especially for people looking into FPGA based development. There is a hardware implementation of a k-distance calculator, sorting and selector. Be sure to read through the text for the sorting algorithm as we found it quite interesting.
Arduino recently released the Arduino MKR4000 board which has an FPGA, and there are many opensource boards out there in the wild that you can easily get started with today. We hope to see some of these in conference badges in the upcoming years.
