[F4HDK] calls his new computer A2Z because he built everything from scratch (literally, from A to Z). Well, strictly speaking, he did start with an FPGA, but you have to have some foundation. The core CPU is a 16-bit RISC processor with a 24-bit address bus and a 128-word cache. The computer sports 2 megabytes of RAM, a boot ROM, a VGA port and keyboard, and some other useful I/O. The CPU development uses Verilog.
Software-wise, the computer has a simple operating system, a filesystem, and basic programs like a text editor and an image viewer. Development software includes an assembler and a compiler for a BASIC-like language that resides on the PC. You can also run an emulator to experiment with A2Z without hardware. You can see a “car game” running on A2Z in the video below. You can also see videos of some other applications.
Continue reading “FPGA Computer Covers A to Z”
If you are from the United States and of a certain age, it is very likely you owned some form of Commodore computer. Outside the US, that same demographic was likely to own an Amstrad. The Z80-based computers were well known for game playing. [Freemac] implemented a working Amstrad CPC6128 using a Xilinx FPGA on a NEXYS2 demo board.
The wiki posting is a bit long, but it covers how to duplicate the feat, and also gives technical details about the design. It also outlines the development process used ranging from starting with a simple Z80 emulation and moving on to more sophisticated attempts. You can see a video of the device below.
Continue reading “Amstrad on an FPGA”
Gravity can be a difficult thing to simulate effectively on a traditional CPU. The amount of calculation required increases exponentially with the number of particles in the simulation. This is an application perfect for parallel processing.
For their final project in ECE5760 at Cornell, [Mark Eiding] and [Brian Curless] decided to use an FPGA to rapidly process gravitational calculations. This allows them to simulate a thousand particles at up to 10 frames per second. With every particle having an attraction to every other, this works out to an astonishing 1 million inverse-square calculations per frame!
The team used an Altera DE2-115 development board to build the project. General operation is run by a Nios II processor, which handles the VGA display, loads initial conditions and controls memory. The FPGA is used as an accelerator for the gravity calculations, and lends the additional benefit of requiring less memory access operations as it runs all operations in parallel.
This project is a great example of how FPGAs can be used to create serious processing muscle for massively parallel tasks. Check out this great article on sorting with FPGAs that delves deeper into the subject. Video after the break.
Continue reading “Gravity Simulations With An FPGA”
Hackaday reader [nats.fr] wrote in with some code from a project that resizes a video stream on the fly using an FPGA. Doing this right means undoing whatever gamma correction has been applied to the original stream, resizing, and then re-applying the gamma. Making life simpler, [nats.fr] settled on a gamma of two, which means taking a bunch of square roots, which isn’t fast on an FPGA.
[nats]’s algorithm is pretty neat: it uses a first-stage lookup to figure out in which broad range the value lies, and then one step of Hero’s algorithm to refine from there. (We think this is equivalent to saying he does a piecewise linear interpolation, but we’re not 100% sure.) Anyway, it works decently.
Of course, when you start looking into the abyss that is special function calculation, you risk falling in. Wikipedia lists more methods of calculating square roots than we have fingers. One of them, CORDIC, avoids even using multiplication by resorting to clever bitshifts and a lookup table. Our go-to in these type of situations, Chebyshev polynomial approximation, didn’t even make the cut. (Although we suspect it would be a contender in the
gamma=2.2 cases, especially if combined with range-reduction in a first stage like [nats.fr] does.)
So what’s the best/fastest approximation for
sqrt(x) for 16-bit integers on an FPGA? [nats.fr] is using a Spartan 6, so you can use a multiplier, but division is probably best avoided. What about arbitrary, possibly fractional, roots?
Need additional, custom IO for your Raspberry Pi? Adding an FPGA is a logical way to expand your IO, and allow for high speed digital interfaces. [Eric Brombaugh]’s Icehat adds a Lattice iCE5LP4K-SG48 FPGA in a package that fits neatly on top of the Raspberry Pi Zero. It also provides a few LEDs and Digilent compatible PMOD connectors for adding peripherals. The FPGA costs about six bucks, so this is one cheap FPGA board.
The FPGA has one time programmable memory, but can also be programmed over SPI. This allows the host Pi to flash the FGPA with the latest bitstream at boot. Sadly, this particular device is not supported by the open source Icestorm toolchain. Instead, you’ll need Lattice’s iCEcube2 design software. Fortunately, this chip is supported by the free license.
Icehat is an open source hardware design, but also includes a software application for flashing a bitstream to the FPGA from the Pi and an example application to get you started. All the relevant sources can be found on Github, and the PCB is available on OSHPark.
While this isn’t the first pairing of a Raspberry Pi and FPGA we’ve seen, it is quite possibly the smallest, and can be built by hand at a low cost.
The Raspberry Pi is the Arduino of 2016, and that means shields, hats, add-ons, and other fun toys that can be plugged right into the GPIO pins of a Pi. For this year’s Hackaday Prize, [Valentin] is combining the Pi with the next age of homebrew computation. He’s developed the Flea Ohm, an FPGA backpack or hat for the Pi Zero.
The Flea Ohm is based on Lattice’s ECP5 FPGA featuring 24k LUTs and 112kB BRAM. That’s enough for some relatively interesting applications, but the real fun comes from the added 32MB or 128MB of SDRAM, a micro SD card slot, USB + PS/2 host port and an LVDS output.
The combination of Raspberry Pis and FPGAs are extremely interesting and seem to be one of the best FPGA learning platforms anyone can imagine. Another Hackaday Prize entry, the ZinqBerry does a similar trick, but instead of a Pi hat, the ZinqBerry drops a Xilinx Zynq with an FPGA and ARM Cortex A9 core onto a board with Ethernet, HDMI, and USB.
If it’s a Flea or a Zinq, the age of FPGA’d Raspberry Pis is quickly approaching, and hopefully we’ll see them as finalists in the Hackaday Prize. You can check out a video of the Flea Ohm below.
Continue reading “Hackaday Prize Entry: FPGAs For The Raspberry Pi Zero”
Playing around with FPGAs used to be a daunting prospect. You had to fork out a hundred bucks or so for a development kit, sign the Devil’s bargain to get your hands on a toolchain, and only then can you start learning. In the last few years, a number of forces have converged to bring the FPGA experience within the reach of even the cheapest and most principled open-source hacker.
[Ken Boak] and [Alan Wood] put together a no-nonsense FPGA board with the goal of getting the price under $30. They basically took a Lattice iCE40HX4K, an STMF103 ARM Cortex-M3 microcontroller, some SRAM, and put it all together on a single board.
The Lattice part is a natural choice because the IceStorm project created a full open-source toolchain for it. (Watch [Clifford Wolf]’s presentation). The ARM chip is there to load the bitstream into the FPGA on boot up, and also brings USB connectivity, ADC pins, and other peripherals into the mix. There’s enough RAM on board to get a lot done, and between the ARM and FPGA, there’s more GPIO pins than we can count.
Modeling an open processor core? Sure. High-speed digital signal capture? Why not. It even connects to a Raspberry Pi, so you could use the whole affair as a high-speed peripheral. With so much flexibility, there’s very little that you couldn’t do with this thing. The trick is going to be taming the beast. And that’s where you come in.