One of the best features of using FPGAs for a design is the inherent parallelism. Sure, you can write software to take advantage of multiple CPUs. But with an FPGA you can enjoy massive parallelism since all the pieces are just hardware. Every light switch in your house operates in parallel with the others. There’s a new edition of a book, titled Parallel Programming for FPGAs that explores that topic in depth and it is under the Creative Commons license. In particular, the book focuses on using Vivado HLS instead of the more traditional Verilog or VHDL.
HLS allows a designer to express a high-level algorithm in C, C++, or SystemC. Given a bit more information, HLS will convert that into an FPGA configuration. That doesn’t mean, though, that you can just cut and paste ordinary C code. HLS has several restrictions due to the fact that it is compiling to logic gates, not lines of code. Actually, it also generates Verilog or VHDL, but if you do it right, that should be transparent to you.
After the introduction, the book is more like a series of monographs on very specific topics, but the depth of each is very impressive. There’s plenty of DSP examples, of course. There’s also general math, so if you ever wondered how to compute a sine or cosine in an FPGA, read chapter 3.
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If you are working with AC circuits a vector network analyzer (VNA) is quite handy. As an entry to the InnovateFPGA competition for students, [Evgenii Vostrikov], [Danila Nikiforovskii], and [Daniil Smirnov] created a VNA using a DE10-Nano, high-speed analog to digital and digital to analog converters, and a circulator. Most of the details are in the video below, and on the project’s GitHub page.
The DE10-Nano has a dual-core ARM processor and an Altera FPGA in one package. That allows you to use the CPUs where that makes sense and still leverage the FPGA where you need high performance.
The circulator uses an op-amp to allow the test signal to route to the device under test, while steering any reflected signal back to the device for measurement. The design also uses a lock-in amplifier, something we’ve talked about a few times recently. This allows less expensive converters to generate magnitude and phase information.
Judging by the fan in the video, we suspect the setup gets a little toasty. The GitHub page has a lot of Russian on it, so we aren’t sure how much we could puzzle out since our Russian skills were mostly from watching the Adventures of Moose and Squirrel.
If you are interested in a VNA, they aren’t as expensive as they used to be. Particularly, if you roll your own and already have some things in your junk box.
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Computers are great at a lot of things. However, general-purpose computers can benefit from help on certain tasks, which is why your video card and sound card both have their own specialized hardware to offload the CPU. If Accelize has its way, some of your hardware acceleration will be done in the cloud. Yes, we know. The cloud is the buzzword of the week and we are tired of hearing about it, too. However, this service is a particularly interesting way to add FPGA power to just about any network-connected CPU.
Currently, there are only four accelerators available, including a hardware-assisted random number generator, a GZIP accelerator, an engine for rapidly searching text, and a BMP to JPEG converter. The company claims, for example, that the search engine can find 2500 entries in the 60 GB Wikipedia archive in 6 minutes. They claim a traditional CPU would take over 16 days to do the same task. The BMP to JPEG converter can process faster than required to feed real-time HD video.
The cloud, in this case, is FPGA resources hosted in the Amazon cloud or in the OVH public cloud. They’ll clearly charge for the service at some point using a “coin” system. However, right now they are letting you sign up with nothing more than an e-mail address and crediting your account with 50,000 coins. Apparently, coins are 1,000 for one dollar.
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Alorium rolled out a new product late last year that caught our attention. The Sno (pronounced like “snow”) board is a tiny footprint Arduino board that you can see in the video below. By itself that isn’t that interesting, but the Sno also has an Altera/Intel Max 10 FPGA onboard. If you aren’t an FPGA user, don’t tune out yet, though, because while you can customize the FPGA in several ways, you don’t have to.
Like Alorium’s XLR8 product, the FPGA comes with preprogrammed functions and a matching Arduino API to use them. In particular, there are modules to do analog to digital conversion, servo control, operate NeoPixels, and do floating point math.
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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.
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Collecting old CPUs and firing them up again is all the rage these days, but how do you know if they will work? For many of these ICs, which ceased production decades ago, sorting the good stuff from the defective and counterfeit is a minefield.
Testing old chips is a challenge in itself. Even if you can find the right motherboard, the slim chances of escaping the effect of time on the components (in particular, capacitor and EEPROM degradation) make a reliable test setup hard to come by.
Enter [Samuel], and the Universal Chip Analyzer (UCA). Using an FPGA to emulate the motherboard, it means the experience of testing an IC takes just a matter of seconds. Why an FPGA? Microcontrollers are simply too slow to get a full speed interface to the CPU, even one from the ’80s.
So, how does it actually test? Synthesized inside the FPGA is everything the CPU needs from the motherboard to make it tick, including ROM, RAM, bus controllers, clock generation and interrupt handling. Many testing frequencies are supported (which is helpful for spotting fakes), and if connected to a computer via USB, the UCA can check power consumption, and even benchmark the chip. We can’t begin to detail the amount of thought that’s gone into the design here, from auto-detecting data bus width to the sheer amount of models supported, but you can read more technical details here.
The Mojo v3 FPGA development board was chosen as the heart of the project, featuring an ATmega32U4 and Xilinx Spartan 6 FPGA. The wily among you will have already spotted a problem – the voltage levels used by early CPUs vary greatly (as high as 15V for an Intel 4004). [Samuel]’s ingenious solution to keep the cost down is a shield for each IC family – each with its own voltage converter.
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Today, Apple is known for iPhones, iPads, and a commitment to graphical user interfaces. But that wasn’t how it all started. The original Apple was a single board computer built around a 6502. In 1976, you could snag one for $666.66, but you needed to supply your own TV, power supply, and keyboard. [Alangarf] didn’t have an Apple 1, but he did have a 6502 CPU core for FPGAs from [Andrew Holme] that he fleshed out to an Apple I clone with a VGA output and PS/2 keyboard port. The project works with either an iCE40 board or a Terasic DE0 board. You could probably port it to other similar FPGAs.
This is much more practical than trying to find an original, as Apple bought a lot of the old boards back and destroyed them. According to the Apple-1 Registry there are only about 71 of the boards still in existence, and that’s with the annotation that 4 of those may be lost and 8 might be duplicates. We’ve heard that of those there are only six that actually still work.
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