The Hackaday community has answered the call and put their computers put to work folding proteins found in the coronavirus. Team_Hack-a-Day ranks #44 in the world so far this month, and I’ve seen us rank as high as #19 on 24-hour leaderboards.
Want to join the fight? Donate some of those computing cycles you’re not using to battling SARS‑CoV‑2. You’re probably not an epidemiologist or a vaccine researcher, but you can make their jobs easier by providing them with the data they need through the Folding@home Project.
As Dan Maloney explained in his excellent article on protein folding, understanding the incredibly complex folding behavior of the proteins in the virus will be key to finding treatments and possibly a vaccine. Folding@home connects countless computers via the internet and is now the largest supercomputer in the world, consisting of over 3.5 million CPUs and over half a million GPUs. The resulting data is freely available to researchers.
Let’s take a look at how easy it is to get up and running, how a GPU can supercharge a setup, and dip into the stats for Team_Hack-a-Day’s effort.
Continue reading “Help Us Throw More Cycles At The Coronavirus Problem”
Another week, another exploit against an air-gapped computer. And this time, the attack is particularly clever and pernicious: turning a GPU into a radio transmitter.
The first part of [Mikhail Davidov] and [Baron Oldenburg]’s article is a review of some of the basics of exploring the RF emissions of computers using software-defined radio (SDR) dongles. Most readers can safely skip ahead a bit to section 9, which gets into the process they used to sniff for potentially compromising RF leaks from an air-gapped test computer. After finding a few weak signals in the gigahertz range and dismissing them as attack vectors due to their limited penetration potential, they settled in on the GPU card, a Radeon Pro WX3100, and specifically on the power management features of its ATI chipset.
With a GPU benchmarking program running, they switched the graphics card shader clock between its two lowest power settings, which produced a strong signal on the SDR waterfall at 428 MHz. They were able to receive this signal up to 50 feet (15 meters) away, perhaps to the annoyance of nearby hams as this is plunk in the middle of the 70-cm band. This is theoretically enough to exfiltrate data, but at a painfully low bitrate. So they improved the exploit by forcing the CPU driver to vary the shader clock frequency in one megahertz steps, allowing them to implement higher throughput encoding schemes. You can hear the change in signal caused by different graphics being displayed in the video below; one doesn’t need much imagination to see how malware could leverage this to exfiltrate pretty much anything on the computer.
It’s a fascinating hack, and hats off to [Davidov] and [Oldenburg] for revealing this weakness. We’ll have to throw this on the pile with all the other side-channel attacks [Samy Kamkar] covered in his 2019 Supercon talk.
Continue reading “GPU Turned Into Radio Transmitter To Defeat Air-Gapped PC”
There was a time when running a program on an array of processors meant that you worked in some high-powered lab somewhere. Now your computer probably has plenty of processors hiding in its GPU and if you have an FPGA, you have everything you need to make something custom. The idea behind TornadoVM is to modify OpenJDK and GraalVM to support running some Java code on parallel architectures supported by OpenCL. The system can utilize multi-core CPUs, GPUs (NVIDIA and AMD), Intel integrated GPUs, and Intel FPGAs.
If you want to try your hand at accelerated Java, there are some docker containers to get you started fast. There’ are also quite a few examples, such as a computer vision application.
Continue reading “Java On GPUs And FPGAs”
A single board computer on a desk is fine for quick demos but for taking it into the wild (or even the rest of the house) you’re going to want a little more safety from debris, ESD, and drops. As SBCs get more useful this becomes an increasingly relevant problem to solve, plus a slick enclosure can be the difference between a nice benchtop hack and something that looks ready to sell as a product. [Chris] (as ProjectSBC) has been working on a series of adaptable cases called the MagClick Case System for the LattePanda Alpha SBC which are definitely worth a look.
The LattePanda Alpha isn’t a run-of-the-mill SBC; it’s essentially the mainboard from a low power ultrabook and contains up to an Intel Core M series processor, 8GB RAM, and 64GB of eMMC. Not to mention an onboard Atmega32u4, WiFi, Gigabit Ethernet, and more. It has more than enough horsepower to be used as an everyday desktop computer or even a light gaming system if you break PCIe out of one the m.2 card slots. But [Chris] realized that such adaptability was becoming a pain as he had to move it from case-to-case as his use needs changed. Thus the MagClick Case System was born.
Continue reading “Magnets Make This Panda Move”
Synthetic-aperture radar, in which a moving radar is used to simulate a very large antenna and obtain high-resolution images, is typically not the stuff of hobbyists. Nobody told that to [Henrik Forstén], though, and so we’ve got this bicycle-mounted synthetic-aperture radar project to marvel over as a result.
Neither the electronics nor the math involved in making SAR work is trivial, so [Henrik]’s comprehensive write-up is invaluable to understanding what’s going on. First step: build a 6-GHz frequency modulated-continuous wave (FMCW) radar, a project that [Henrik] undertook some time back that really knocked our socks off. His FMCW set is good enough to resolve human-scale objects at about 100 meters.
Moving the radar and capturing data along a path are the next steps and are pretty simple, but figuring out what to do with the data is anything but. [Henrik] goes into great detail about the SAR algorithm he used, called Omega-K, a routine that makes use of the Fast Fourier Transform which he implemented for a GPU using Tensor Flow. We usually see that for neural net applications, but the code turned out remarkably detailed 2D scans of a parking lot he rode through with the bike-mounted radar. [Henrik] added an auto-focus routine as well, and you can clearly see each parked car, light pole, and distant building within range of the radar.
We find it pretty amazing what [Henrik] was able to accomplish with relatively low-budget equipment. Synthetic-aperture radar has a lot of applications, and we’d love to see this refined and developed further.
When shopping online, there’s plenty of great deals out there on modern graphics hardware. Of course, if you’re like [Dawid] and bought a GTX1050 Ti for $48 from Wish, you probably suspect it’s too good to be true. Of course, you’d be correct.
[Dawid] notes from the outset that the packaging the card ships in is unusual. While it’s covered in NVIDIA and GeForce branding, there’s no note of the model number or even the overarching series. The card is loosely packed in bubblewrap, free to bounce around in transit. Upon installation, the card reports itself as a GTX1050 Ti, but refuses to properly work with NVIDIA drivers and routinely causes a Blue Screen of Death.
Upon disassembly, it becomes apparent that the card is merely a poorly manufactured GTS450 Revision 2, over five generations older than the card it was advertised as. Thanks to the mismatch between the actual hardware and what the card reports as, the drivers are unable to properly work with the card.
For those that have been scammed, there is some hope. [Phil] has had experience with several of these cards, which similarly misreport their actual hardware. To correct this, the cards need to have their BIOS flashed to reflect reality, but the fake cards don’t work with NVIDIA’s NVFlash tool. Instead, they must be flashed manually using an EEPROM programmer. Once the cards are flashed with an appropriate BIOS, they can be used with the proper drivers and will function properly, albeit with much less performance than was advertised.
It’s an interesting insight into the state of online shopping platforms, and the old adage remains true – if it’s too good to be true, it probably is. Plus, hacking GPUs can often have great results. Video after the break.
Continue reading “Fake Graphics Cards And How To Fix Them”
The engineers and product designers at [moovel lab] have created the Open Data Cam – an AI camera platform that can identify and count objects as they move through its field of view – along with an open source guide for making your own.
Step one: get out your ruler and utility knife. In this world of ubiquitous 3D-printers they’ve taken a decidedly low-tech approach to the project’s enclosure: a cut, folded, and zip-tied plastic box, with a cardboard frame inside to hold the electronic bits. It’s “splash proof” and certainly cheap to make, but we’re a little worried about cooling and physical protection for the electronics inside, as they’re not exactly cheap and rugged components.
So what’s inside? An Nvidia Jetson TX2 board, a LiPo battery with some charging circuitry, and a standard webcam. The special sauce, however, is the software, which is available on GitHub. [Moovel lab]’s engineers have put together a nice-looking wifi-accessible mobile UI for marking the areas where you’d like the software to identify and tally objects. The actual object detection and identification tasks are performed by the speedy YOLO neural network, a task the Nvidia board’s GPU is of course well suited for.
As the Open Data Cam’s unblinking glass eye gazes upon our urban environments, it will log its observations in an ancient and mysterious language: CSV. It’s up to you, human, to interpret this information and use it for good.
A summary video and build time lapse are embedded after the break.
Continue reading “Open Data Cam Combines Camera, GPU, And Neural Network In An Artisanal DIY Cereal Box”