Neural Network Gimbal Is Always Watching

[Gabriel] picked up a GoPro to document his adventures on the slopes and trails of Montreal, but quickly found he was better in front of the camera than behind it. Turns out he’s even better seated behind his workbench, as the completely custom auto-tracking gimbal he came up with is nothing short of a work of art.

There’s quite a bit going on here, and as you might expect, it took several iterations before [Gabriel] got all the parts working together. The rather GLaDOS-looking body of the gimbal is entirely 3D printed, and holds the motors, camera, and a collection of ultrasonic receivers. The Nvidia Jetson TX1 that does the computational heavy lifting is riding shotgun in its own swanky looking 3D printed enclosure, but [Gabriel] notes a future revision of the hardware should be able to reunite them.

In the current version of the system, the target wears an ultrasonic emitter that is picked up by the sensors in the gimbal. The rough position information provided by the ultrasonics is then refined by the neural network running on the Jetson TX1 so that the camera is always focused on the moving object. Right now the Jetson TX1 gets the video feed from the camera over WiFi, and commands the gimbal hardware over Bluetooth. Once the Jetson is inside the gimbal however, some of the hardware can likely be directly connected, and [Gabriel] says the ultrasonics may be deleted from the design completely in favor of tracking purely in software. He plans on open sourcing the project, but says he’s got some internal house keeping to do before he takes the wraps off it.

From bare bones to cushy luxury, scratch-built camera gimbals have become something of a right of passage for the photography hacker. But with this project, it looks like the bar got set just a bit higher.

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Hackaday Links: August 27, 2017

Hulk Hands! Who remembers Hulk Hands? These were a toy originally released for the 2003 Hulk movie and were basically large foam clenched fists you could wear. Hulk Hands have been consistently been re-released for various Marvel films, but now there’s something better: it’s the stupidest tool ever. Two guys thought it would be fun and not dangerous at all to create cast iron Hulk Hands and use them as demolition and renovation equipment. This is being sold as a tool comparable to a sledgehammer or a wrecking bar.

New Pogs! We’re up to 0x0C. Is your collection complete?

[Peter] is building an airplane out of foam in his basement. He’s also doing it as a five or six-part series on his YouTube channel. Part two is now up. This update covers the tail surfaces, weighing and balancing the fuselage, and a general Q&A with YouTube comments.  Yes, [Peter] still has a GoFundMe up for a parachute, and it’s already about half funded. With any luck, he’ll have the $2600 for a parachute before he builds the rest of the plane. Another option is a ballistic parachute system — a parachute for the whole plane, like a Cirrus. That would be a bit more than $4000, so we’ll see how far the GoFundMe goes.

Hey, remember the Nvidia Jetson TX1? It’s a miniATX motherboard running a fast ARM core with a GPU housing 256 CUDA cores. It’s cool, and the new version — the TX2 — is designed for ‘machine learning at the edge’. They’re on sale now, for only $199.

Primitive Technology has another video out. This time, he’s improving his bow string blower into something that kinda, sorta resembles a modern forge. This time, the experiment was a success when it comes to pottery — he’s now able to fire clay at a much higher temperature, bringing him reasonably close to modern ceramics. At least, as close as you can get starting with the technology of a pointed stick. The experiment was marginally successful when it came to creating iron. He’s using iron-bearing bacteria (!) for his source of ore and was able to smelt millimeter-sized pellets of iron. This guy needs a source of copper or tin. Zinc is also surprisingly possible given his new found capabilities for ceramics.

Hands-On Nvidia Jetson TX2: Fast Processing for Embedded Devices

The review embargo is finally over and we can share what we found in the Nvidia Jetson TX2. It’s fast. It’s very fast. While the intended use for the TX2 may be a bit niche for someone building one-off prototypes, there’s a lot of promise here for some very interesting applications.

Last week, Nvidia announced the Jetson TX2, a high-performance single board computer designed to be the brains of self-driving cars, selfie-snapping drones, Alexa-like bots for the privacy-minded, and other applications that require a lot of processing on a significant power budget.

This is the follow-up to the Nvidia Jetson TX1. Since the release of the TX1, Nvidia has made some great strides. Now we have Pascal GPUs, and there’s never been a better time to buy a graphics card. Deep learning is a hot topic that every new CS grad wants to get into, and that means racks filled with GPUs and CUDA cores. The Jetson TX1 and TX2 are Nvidia’s strike at embedded deep learningor devices that need a lot of processing power without sucking batteries dry.

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Neural Network Targets Cats with a Sprinkler System

It’s overkill, but it’s really cool. [Bob Bond] took an NVIDIA Jetson TX1 single-board computer and a webcam and wirelessly combined them with his lawn sprinklers. Now, when his neighbors’ cats come to poop in his yard, a carefully trained neural network detects them and gets them wet.

It is absolutely the case that this could have been done with a simple motion sensor, but if the neural network discriminates sufficiently well between cats and (for instance) his wife, this is an improved solution for sure. Because the single-board computer he’s chosen for the project has a ridiculous amount of horsepower, he can afford to do a lot of image processing, so there’s a chance that everyone on two legs will stay dry. And the code is up on GitHub for you to see, if you’re interested.

[Bob] promises more detail about the neural network in the future. We can’t wait. (And we’d love to see a sentry-turret style build in the future. Think of the water savings!)

Via the NVIDIA blog, and thanks [Jaqen] for the tip!

The Nvidia Jetson TX1: It’s Not For Everybody, But It Is Very Cool

Last week, the Nvidia Jetson TX1 was released. This credit card-sized module is a ‘supercomputer’ advertised as having more processing power than the latest Intel Core i7s, while running at under 10 Watts. This is supposedly the device that will power the next generation of things, using technologies unheard of in the embedded world.

A modern day smartphone could have been built 10 or 15 years ago. There’s no question the processing power was there with laptop CPUs, and the tiny mechanical hard drives in the original iPod was more than spacious enough to hold a library of Napster’d MP3s and all your phone contacts. The battery for this sesquidecadal smartphone, on the other hand, was impossible. The future depends on batteries and consequently low power computing. Is the Jetson TX1 the board that will deliver us into the future? It took a hands-on look to find out.

Nvidia-2
The Nvidia Jetson TX1 and Carrier Board

What is the TX1

The Jetson TX1 is a tiny module – 50x87mm – encased in a heat sink that brings the volume to about the same size as a pack of cigarettes. Underneath a block of aluminum is an Nvidia Tegra X1, a module that combines a 64-bit quad-core ARM Cortex-A57 CPU with a 256-core Maxwell GPU. The module is equipped with 4GB of LPDDR4-3200, 16GB of eMMC Flash, 802.11ac WiFi, and Bluetooth.

This module connects to the outside world through a 400-pin connector (from Samtec, a company quite liberal with product samples, by the way) that provides six CSI outputs for a half-dozen Raspberry Pi-style cameras, two DSI outputs, 1 eDP 1.4, 1 eDP 1.2, and HDMI 2.0 for displays. Storage is provided through either SD cards or SATA. Other ports include three USB 3.0, three USB 2.0, Gigabit Ethernet, a PCIe x1 and PCIe x4, and a host of GPIOs, UARTs, SPI and I2C busses.

The only way of getting at all these extra ports is, at the moment, the Jetson TX1 carrier board, a board that is effectively a MiniITX motherboard. Mount this carrier board in a case, modify a power supply and figure out how to wire up the front panel buttons, and you’ll have a respectable desktop computer.

This is not a desktop computer, though, and it’s not a replacement for a Raspberry Pi or Beaglebone. This is an engineering tool – a device built to handle the advanced robotics work of the future.

Benchmarks

No tech review would be complete without benchmarks, and since this is an Nvidia board, that means a deep dive into the graphics performance.

The review unit Nvidia sent over came with an incredible amount of documentation, pointing me towards GFXBench 4.0 Manhattan 3.1 (and the T-rex one) to test the graphics performance.

Manhattan

In terms of graphics performance, the TX1 isn’t that much different from a run-of-the-mill mobile chipset from a few years ago. This is to be expected; it’s unreasonable to expect Nvidia to put a Titan in a 10 Watt module; the Titan itself sucks up about 250 Watts.

test suiteWhat about CPU performance? The ARM Cortex A57 isn’t seen very much in tiny credit-card sized dev boards, but there are a few actual products out there with it.  The TX1 isn’t a powerhouse by any means, but it does trounce the Raspberry Pi 2 Model B in testing by a factor of about three.

Compared to desktop/x86 performance, the best benchmarks again put the Nvidia TX1 in the same territory as a middling desktop from a few years ago. Still, that desktop probably draws about 300 W total, where the TX1 sips a meager 10 W.

This is not the board you want if you’re mining Bitcoins, and it’s not the board you should use if you need a powerful, portable device that can connect to anything. It’s for custom designs. The Nvidia TX1 is a module that’s meant to be integrated into products. It’s not a board for ‘makers’ and it’s not designed to be. It’s a board for engineers that need enough power in a reasonably small package that doesn’t drain batteries.

With an ARM Cortex A57 quad core running at almost 2 GHz, 4 GB of RAM, and a reasonably powerful graphics card for the power budget, the Nvidia TX1 is far beyond the usual tiny Linux boards. It’s far beyond the Raspi, the newest Beagleboard, and gives the Intel NUC boards a run for their money.

That huge and heavy heatsink is useful; while benchmarking the TX1, temperatures were only one or two degrees above ambient
That huge and heavy heatsink is useful; while benchmarking the TX1, temperatures were only one or two degrees above ambient

In terms of absolute power, the TX1 is about as powerful as a entry-level laptop from three or four years ago.

The Jetson TX1 is all about performance per Watt. That’s exceptional, new, and exciting; it’s something that simply hasn’t been done before. If you believe the reams of technical documents Nvidia granted me access to, it’s the first step to a world of truly smart embedded devices that have a grasp on computer vision, machine learning, and a bunch of other stuff that hasn’t really found its way into the embedded world yet.

Alexnex images processed per second per watt. No, Joules do not exist.
Alexnex images processed per second per Watt. No, Joules do not exist.

And here lies the problem with the Jetson TX1; because a platform like this hasn’t been available before, the development stack, examples, and community of users simply isn’t there yet. The number of people contributing to the Nvidia embedded systems forum is tiny – our Hackaday articles get more comments than a thread on the Nvidia forums. Like all new platforms, the only thing missing is the community, putting Nvidia in a chicken and egg scenario.

This a platform for engineers. Specifically, engineers who are building autonomous golf carts and cars, quadcopters that follow you around, and robots that could pass a Turing test for at least 30 seconds. It’s an incredible piece of hardware, but not one designed to be a computer that sits next to a TV. The TX1 is an engineering tool that’s meant to go into other devices.

Alternative Applications, Like Gamecube

With that said, there are a few very interesting applications I could see the TX1 being used for. My car needs a new head unit, and building one with the TX1 would future proof it for at least another 200,000 miles. For the very highly skilled amateur engineers, the TX1 module opens a lot of doors. Six webcams is something a lot of artists would probably like to experiment with, and two DSI outputs – and a graphics card – would allow for some very interesting user interfaces.

That said, the TX1 carrier board is not the breakout board for these applications. I’d like to see something like what Sparkfun put together for the Intel Edison – dozens of breakout boards for every imaginable use case. The PCB files for the TX1 carrier board are available through the Nvidia developer’s portal (hope you like OrCAD), and Samtec, the supplier for the 400-pin connector used for the module, is exceedingly easy to work with. It’s not unreasonable for someone with a reflow toaster oven to create a breakout for the TX1 that’s far more convenient than a Mini-ITX motherboard.

Right now there aren’t many computers with ARM processors and this amount of horsepower out now. Impressively powerful ARM boards, such as the new BeagleBoard X15 and those that follow the 96Boards specification exist, but these do not have a modern graphics card baked into the module.

Without someone out there doing the grunt work of making applications with mass appeal work with the TX1, it’s impossible to say how well this board performs at emulating a GameCube, or any other general purpose application. The hardware is probably there, but the reviewers for the TX1 have been given less than a week to StackOverflow their way through a compatible build for the most demanding applications this board wasn’t designed for.

It’s all about efficiency

Is the TX1 a ‘supercomputer on a module’? Yes, and no. While it does perform reasonably well at machine learning tasks compared to the latest core-i7 CPUs, the Alexnet machine learning tasks are a task best suited for GPUs. It’s like asking which flies better: a Cessna 172 or a Bugatti Veyron? The Cessna is by far the better flying machine, but if you’re looking for a ‘supercomputer’, you might want to look at a 747 or C-5 Galaxy.

On the other hand, there aren’t many boards or modules out there at the intersection of high-powered ARM boards with a GPU and on a 10 Watt power budget. It’s something that’s needed to build the machines, robots, and autonomous devices of the future. But even then it’s still a niche product.

I can’t wait to see a community pop up around the TX1. With a few phone calls to Samtec, a few hours in KiCad, and a group buy for the module itself ($299 USD in 1000 unit quantities), this could be the start of something very, very interesting.