Several months ago, a strange Kickstarter project from ‘Team IoT’ appeared that seemed too good to be true. The Atomic Pi was billed as a high-power alternative to the Raspberry Pi, and the specs are amazing. For thirty five American buckaroos, you get a single board computer with an Intel processor. You get 16 Gigs of eMMC Flash, more than enough for a basic Linux system and even a cut-down version of Windows 10. You have WiFi, you have Bluetooth, you have a real time clock, something so many of the other single board computers forget. The best part? It’s only thirty five dollars.
Naturally, people lost their minds. There are many challengers to the Raspberry Pi, but nothing so far can beat the Pi on both price and performance. Could the Atomic Pi be the single board computer that finally brings the folks from Cambridge to their knees? Is this the computer that will revolutionize STEM education, get on a postage stamp, and sell tens of millions of units?
No. The answer is no. While I’m not allowed to call the Atomic Pi “literal garbage” because our editors insist on the technicality that it’s “surplus” because they were purchased before they hit the trash cans, there will be no community built around this thirty five dollar single board computer. This is a piece of electronic flotsam that will go down in history right next to the Ouya console. There will be no new Atomic Pis made, and I highly doubt there will ever be any software updates. Come throw your money away on silicon, fiberglass and metal detritus! Or maybe you have a use for this thing. Meet the Atomic Pi!
Continue reading “The Atomic Pi: Is It Worth It?”
Robot cars, DIY or otherwise, are hot right now. To do this right, you’re going to need cameras, LIDAR, or some other way of sensing the the world. Intel is again getting into the fray with a RealSense tracking camera for simultaneous localization and mapping for robotics, drone, and augmented reality needs.
The tech specs for the Intel RealSense T265 are impressive for small robotics uses. It includes 6DoF tracking gathered by two cameras, each with a 170° FoV. Connection to a computer is through USB 2.0 or 3.0. If you want to get an idea of how seriously Intel is taking the ‘robotics, and other power- and weight-limited platforms’ market, here’s a sample of what is on the one-page spec sheet: the T265 only uses 1.5 Watts, weighs 55 grams, and is 108 x 25 x 13 mm. There are also two M3 taps spaced 50mm apart on the back, which is an astonishing spec to publish on the product landing page. Simply the fact that the location and dimensions of the mounting holes is so prominent gives you an idea of how seriously Intel is taking robotics and prototyping applications.
This new SLAM camera complements Intel’s other tracking camera offerings, including those we’ve seen at Maker Faires past. It’s a competitor to the new crop of solid state LIDAR modules we’ve seen pop up recently. It’s not a Kinect, but we’re years past using a first-gen Kinect for robotics applications. Now, everything is custom chips and SLAM processing, and the RealSense T265 is the smallest platform to do that now.
As a layperson reading about some branches of mathematics, it often seems like mathematicians are just people who really like to create and solve puzzles. And, knowing that computer science shares a lot of its fundamentals with mathematics, we can assume that most computer scientists are also puzzle-solvers as well. This latest project from [tom7] shows off his puzzle creating and solving skills with a readable file which is also a paper, which is also a compiler for C programs, which can also play music.
[tom7] started off with the instruction set for the Intel 8086 processor. Of the instructions available, he wanted to use only instructions which are also readable in a text file. This limits him dramatically in what this file will be able to execute, but also sets up the puzzle. He walks through each of the hurdles he found by only using instructions that also code to text, including limited memory space, no obvious way of exiting the program once it was complete, not being able to jump backward in the program (i.e. looping), and a flurry of other issues that come up once the instruction set is limited in this way.
The result is a sort of C compiler which might not be the most efficient way of executing programs, but it sure is the most effective way of showing off [tom7]’s PhD in computer science. As a bonus, the file can also play an antiquated type of sound file due to one of the available instructions being a call for the processor to interact with I/O. If you want to learn a little bit more about compilers, you can check out a primer we have for investigating some of their features.
Thanks to [Greg] for the tip!
Continue reading “A Compiler In Plain Text Also Plays Music”
Intel just announced their new Sunny Cove Architecture that comes with a lot of new bells and whistles. The Intel processor line-up has been based off the Skylake architecture since 2015, so the new architecture is a fresh breath for the world’s largest chip maker. They’ve been in the limelight this year with hardware vulnerabilities exposed, known as Spectre and Meltdown. The new designs have of course been patched against those weaknesses.
The new architecture (said to be part of the Ice Lake-U CPU) comes with a lot of new promises such as faster core, 5 allocation units and upgrades to the L1 and L2 caches. There is also support for the AVX-512 or Advanced Vector Extensions instructions set which will improve performance for neural networks and other vector arithmetic.
Another significant change is the support for 52-bits of physical space and 57 bits of linear address support. Today’s x64 CPUs can only use bit 0 to bit 47 for an address space spanning 256TB. The additional bits mean a bump to a whooping 4 PB of physical memory and 128 PB of virtual address space.
The new offering was demoed under the company’s 10nm process which incidentally is the same as the previously launched Cannon Lake. The new processors are due in the second half of 2019 and are being heavily marketed as a boon for the Cryptography and Artificial Intelligence Industries. The claim is that for AI, memory to CPU distance has been reduced for faster access, and that special cryptography-specific instructions have been added.
Last time I talked about how I took the open source Verifla logic analyzer and modified it to have some extra features. As promised, this time I want to show it in action, so you can incorporate it into your own designs. The original code didn’t actually capture your data. Instead, it created a Verilog simulation that would produce identical outputs to your FPGA. If you were trying to do some black box simulation, that probably makes sense. I just wanted to view data, so I created a simple C program that generates a VCD file you can read with common tools like
gtkwave. It is all on GitHub along with the original files, even though some of those are not updated to match the new code (notably, the PDF document and the examples).
If you have enough pins, of course, you can use an external logic analyzer. If you have enough free space on the FPGA, you could put something like SUMP or SUMP2 in your design which would be very flexible. However, since these analyzers are made to be configurable from the host computer, they probably have a lot of circuitry that will compete with yours for FPGA space. You configure Verifla at compile time which is not as convenient but lets it have a smaller footprint.
Continue reading “X-Ray Vision For FPGAs: Using Verifla”
All of the tools you need to work with the FPGA Arduino — the Vidor — are now in the wild!
We reported earlier that a series of French blog posts finally showed how all the pieces fit together to program the FPGA on the Arduino MKR4000 Vidor board. Of course, I wasn’t content to just read the Google translation, I had to break out the board and try myself.
I created a very simple starter template, a tool in C to do the bitstream conversion, required, and bundled it all together in one place. Here’s how you can use my starter kit to do your own FPGA designs using the Vidor. I’m going to assume you know about FPGA basics and Verilog. If you don’t, why not check out the FPGA boot camps first?
The first thing you’ll want to do is grab my GitHub repo. You’ll also need the Arduino IDE (a recent copy) and Intel’s Quartus software. Inside, you’ll find three directories, two of which contain slightly modified copies of original Arduino files. But before you start digging in, let’s get the high-level overview of the process.
Continue reading “Hands On With The Arduino FPGA”
The GePS is a musical project that shows how important integration work is when it comes to gesture controls. Creators [Cedric Spindler] and [Frederic Robinson] demonstrate how the output of a hand-mounted IMU (Inertial Measurement Unit) and magnetometer can be used to turn motion, gestures, and quick snap movements into musical output. The GePS is designed to have enough repeatability and low enough latency that feedback is practically immediate. As a result, it can be used and played like any other musical instrument that creates sound from physical movements in a predictable way. It’s not unlike a Theremin in that way, but much more configurable.
To do this, [Cedric] and [Frederic] made GePS from a CurieNano board (based on Intel’s Curie, which also has the IMU on-board) and an XBee radio for a wireless connection to software running on a computer, from which the sounds are played. The device’s sensitivity and low lag means that even small movements can be reliably captured, meaning that the kind of fluid and complex movements that hands do every day can be used as the basis for playing sounds with immediate feedback. In a very real sense, the glove-based GePS is an experimental kind of new instrument, which makes it a fascinating contender for the Musical Instrument Challenge portion of the 2018 Hackaday Prize.