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.
Continue reading “FPGA used VHDL for Fractals”
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”
In a surprising move, ARM has made two Cortex-M cores available for FPGA development at no cost.
In the over three decades since [Sophie Wilson] created the first ARM processor design for the Acorn Archimedes home computer, the architecture has been managed commercially such that it has become one of the most widely adopted on the planet. From tiny embedded microcontrollers in domestic appliances to super-powerful 64-bit multi-core behemoths in high-end mobile phones, it’s certain you’ll own quite a few ARM processors even if you don’t realise it. Yet none of those processors will have been made by ARM, instead the Cambridge-based company will have licenced the intellectual property of their cores to another semiconductor company who will manufacture the device around it to their specification. ARM core licences cost telephone-number sums, so unless you are a well-financed semiconductor company, until now you probably need not apply.
You will still have to shell out the dough to get your hands on a core for powerful chips like those smartphone behemoths, but if your tastes are more modest and run only to a Cortex M1 or M3 you might be in luck. For developers on Xilinx FPGAs they have extended the offer of those two processor cores at zero cost through their DesignStart Programme.
It’s free-as-in-beer rather than something that will please open-source enthusiasts, But it’s certainly a fascinating development for experimenters who want to take ARM for a spin on their own gate array. Speculation is swirling that this is a response to RISC-V, but we suspect it may be more of a partial lifting of the skirts to entice newbie developers such as students or postgraduates. If you arrive in the world of work already used to working with ARM IP at the FPGA level then you are more likely to be on their side of the fence when those telephone-number deals come up.
Thanks [Rik] for the tip!
Although there are a few exceptions, FPGAs are predominantly digital devices. However, many FPGA applications process analog data, so you often see an FPGA surrounded by analog and digital converters. This is so common that Opal Kelly — a producer of FPGA tools — launched the SYZYGY open standard for interconnecting devices like that. [Armeen] — a summer intern at Opal Kelly — did a very interesting open source FPGA-based signal generator using a Xilinx FPGA, and a SYZYGY-compliant digital to analog converter.
As you might expect, [Armeen] used a lot of Opal Kelly hardware and software in the project. But the Verilog code (available on GitHub) shows a lot of interesting things including some very practical example code for using Xilinx CORDIC IP, which is a great way to do high-order math using digital logic.
Continue reading “Signal Generator Uses FPGA”
There are a bunch of FPGA development boards to choose from, but how many will fit inside your laptop? The PicoEVB is a tiny board that connects to a M.2 slot and provides an evaluation platform for the Xilinx Artix-7 FPGA family.
This minimalist board sports a few LEDs, a PCIe interface, an integrated debugger, on-board EEPROM, and some external connectors for hooking up other bits and pieces. The M.2 connector provides the board with power, USB for debugging, and PCIe for user applications.
A major selling point of this board is the PCIe interface. Most FPGA boards with PCIe will cost over a grand, and will only fit in a large desktop computer. The lower priced options use older FPGAs. The PicoEVB is tiny and retails for $219. Not a bad deal when the FPGA on-board costs nearly $100.
The PicoEVB is also open source. Design files and sample projects can be found on Github.
[Thanks to Adam Hunt for the tip!]
They probably weren’t inspired by [Jeff Dunham’s] jalapeno on a stick, but Intel have created the Movidius neural compute stick which is in effect a neural network in a USB stick form factor. They don’t rely on the cloud, they require no fan, and you can get one for well under $100. We were interested in [Jeff Johnson’s] use of these sticks with a Pynq-Z1. He also notes that it is a great way to put neural net power on a Raspberry Pi or BeagleBone. He shows us YOLO — an image recognizer — and applies it to an HDMI signal with the processing done on the Movidius. You can see the result in the first video, below.
At first, we thought you might be better off using the Z1’s built-in FPGA to do neural networks. [Jeff] points out that while it is possible, the Z1 has a lower-end device on it, so there isn’t that much FPGA real estate to play with. The stick, then, is a great idea. You can learn more about the device in the second video, below.
Continue reading “Neural Networks… On a Stick!”