Jetson

9 Articles

Hands-On: NVIDIA Jetson Orin Nano Developer Kit

March 21, 2023 by 35 Comments

NVIDIA’s Jetson line of single-board computers are doing something different in a vast sea of relatively similar Linux SBCs. Designed for edge computing applications, such as a robot that needs to perform high-speed computer vision while out in the field, they provide exceptional performance in a board that’s of comparable size and weight to other SBCs on the market. The only difference, as you might expect, is that they tend to cost a lot more: the current top of the line Jetson AGX Orin Developer Kit is $1999 USD

Luckily for hackers and makers like us, NVIDIA realized they needed an affordable gateway into their ecosystem, so they introduced the $99 Jetson Nano in 2019. The product proved so popular that just a year later the company refreshed it with a streamlined carrier board that dropped the cost of the kit down to an incredible $59. Looking to expand on that success even further, today NVIDIA announced a new upmarket entry into the Nano family that lies somewhere in the middle.

While the $499 price tag of the Jetson Orin Nano Developer Kit may be a bit steep for hobbyists, there’s no question that you get a lot for your money. Capable of performing 40 trillion operations per second (TOPS), NVIDIA estimates the Orin Nano is a staggering 80X as powerful as the previous Nano. It’s a level of performance that, admittedly, not every Hackaday reader needs on their workbench. But the allure of a palm-sized supercomputer is very real, and anyone with an interest in experimenting with machine learning would do well to weigh (literally, and figuratively) the Orin Nano against a desktop computer with a comparable NVIDIA graphics card.

We were provided with one of the very first Jetson Orin Nano Developer Kits before their official unveiling during NVIDIA GTC (GPU Technology Conference), and I’ve spent the last few days getting up close and personal with the hardware and software. After coming to terms with the fact that this tiny board is considerably more powerful than the computer I’m currently writing this on, I’m left excited to see what the community can accomplish with the incredible performance offered by this pint-sized system.

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Turing Pi 2: The Low Power Cluster

June 16, 2022 by 39 Comments

We’re not in the habit of recommending Kickstarter projects here at Hackaday, but when prototype hardware shows up on our desk, we just can’t help but play with it and write it up for the readers. And that is exactly where we find ourselves with the Turing Pi 2. You may be familiar with the original Turing Pi, the carrier board that runs seven Raspberry Pi Compute boards at once. That one supports the Compute versions 1 and 3, but a new design was clearly needed for the Compute Module 4. Not content with just supporting the CM4, the developers at Turing Machines have designed a 4-slot carrier board based on the NVIDIA Jetson pinout. The entire line of Jetson devices are supported, and a simple adapter makes the CM4 work. There’s even a brand new module planned around the RK3588, which should be quite impressive.

One of the design decisions of the TP2 is to use the mini-ITX form-factor and 24-pin ATX power connection, giving us the option to install the TP2 in a small computer case. There’s even a custom rack-mountable case being planned by the folks over at My Electronics. So if you want 4 or 8 Raspberry Pis in a rack mount, this one’s for you.
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NVIDIA Unveils Jetson AGX Orin Developer Kit

March 22, 2022 by 14 Comments

When you think of high-performance computing powered by NVIDIA hardware, you probably think of applications leveraging the capabilities of the company’s graphics cards. In many cases, you’d be right. But naturally there are situations where the traditional combination of x86 computer and bolt-on GPU simply isn’t going to cut it; try packing a modern gaming computer onto a quadcopter and let us know how it goes.

For these so-called “edge computing” situations, NVIDIA offers the Jetson line of ARM single-board computers which include a scaled-down GPU that gives them vastly improved performance for machine learning applications than something like the Raspberry Pi. Today during their annual GPU Technology Conference (GTC), NVIDIA announced the immediate availability of the Jetson AGX Orin Developer Kit, which the company promises can deliver “server-class AI performance” in a package small enough for use in IoT or robotics.

As with the earlier Jetsons, the palm-sized development kit acts as a sort of breakout board for the far smaller module slotted into it. This gives developers access to the full suite of the connectivity and I/O options offered by the Jetson module in a desktop-friendly form that makes prototyping the software side of things much easier. Once the code is working as intended, you can simply pop the Jetson module out of the development kit and install it in your final hardware.

NVIDIA is offering the Orin module in a range of configurations, depending on your computational needs and budget. At the high end is the AGX Orin 64 GB at $1599 USD; which offers a 12-core ARM Cortex-A78AE processor, 32 GB of DDR5 RAM, 64 GB of onboard flash, and a Ampere GPU with 2048 CUDA cores and 64 Tensor cores, which all told enables it to perform an incredible 275 trillion operations per second (TOPS).

At the other end of the spectrum is the Orin NX 8 GB, a SO-DIMM module that delivers 70 TOPS for $399. It’s worth noting that even this low-end flavor of the Orin is capable of more than double the operations per second as 2018’s Jetson AGX Xavier, which until now was the most powerful entry in the product line.

The Jetson AGX Orin Developer Kit is available for $1,999 USD, and includes the AGX Orin 64 GB module. Interestingly, NVIDIA says the onboard software is able to emulate any of of the lower tier modules, so you won’t necessarily have to swap out the internal modules if your final hardware will end up using one of the cheaper modules. Of course the inverse of that is even folks who only planned on using the more budget-friendly units either have to shell out for an expensive dev kit, or try to spin their own breakout board.

While the $50 USD Jetson Nano is far more likely to be on the workbench of the average Hackaday reader, we have to admit that the specs of these new Orin modules are very exciting. Then again, we’ve covered several projects that used the previously top-of-the-line Jetson Xavier, so we don’t doubt one of you is already reaching for their wallet to pick up this latest entry into NVIDIA’s line of diminutive powerhouses.

video of someone pushing the button to generate new art

AI Generating Paintings Off To A Flying Art

August 27, 2021 by 14 Comments

The philosophical question of “What is art?” has an ethereal, transient quality to it. A definition seems to slip away as you get close to an answer. Embracing that quality, [Max Fischer] has created an AI-powered painting that paints a new piece of art at the push of a button. When the button below the screen is pushed, a new image is generated and the old one is forever lost, which in a way, makes the frame a piece of art itself.

The really makes this project stand is the sheer quality of documentation on the GitHub repo. The instructions are incredibly detailed. Everything from setting up the Jetson to building the control box out of half-inch MDF (12mm for the sane part of the world) is laid out with copious pictures. Despite the ease of generating images ahead of time, [Max] took the hard route Hackaday route and did all inference locally and in real-time. To handle the processing requirements, an Nvidia Jetson Xavier NX single-board computer was used. He trained StyleGAN with high-resolution abstract art that gets generated whenever the button below the screen is pushed. To prevent screen burn-in, a PIR was added to turn the screen off when no one is around.

Here at Hackaday, we’ve seen several projects putting old laptop screens or monitors into a nice wooden case and mounting them to the wall. Since 32″ laptops are rather hard to find, [Max] opted to take a different approach and instead got a 32″ Samsung Frame for relatively cheap.

For all their detail, [Max] did leave one thing out of the readme: the AI that generates the art. [Max] hints that he wants others to create their picture frames, but with their own art generation. So what are you waiting for? Go make some art.

Machine Learning Helps You Track Your Internet Misery Index

February 14, 2021 by 8 Comments

We all seem to intuitively know that a lot of what we do online is not great for our mental health. Hang out on enough social media platforms and you can practically feel the changes your mind inflicts on your body as a result of what you see — the racing heart, the tight facial expression, the clenched fists raised in seething rage. Not on Hackaday, of course — nothing but sweetness and light here.

That’s all highly subjective, of course. If you’d like to quantify your online misery more objectively, take a look at the aptly named BrowZen, a machine learning application by [Nick Bild]. Built around an NVIDIA Jetson Xavier NX and a web camera, BrowZen captures images of the user’s face periodically. The expression on the user’s face is classified using a facial recognition model that has been trained to recognize facial postures related to emotions like anger, surprise, fear, and happiness. The app captures your mood and which website you’re currently looking at and stores the results in a database. Handy charts let you know which sites are best for your state of mind; it’s not much of a surprise that Twitter induces rage while Hackaday pushes [Nick]’s happiness button. See? Sweetness and light.

Seriously, we could see something like this being very useful for psychological testing, marketing research, or even medical assessments. This adds to [Nick]’s array of AI apps, which range from tracking which surfaces you touch in a room to preventing you from committing a fireable offense on a video conference.

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Jetson Emulator Gives Students A Free AI Lesson

October 26, 2020 by 4 Comments

With the Jetson Nano, NVIDIA has done a fantastic job of bringing GPU-accelerated machine learning to the masses. For less than the cost of a used graphics card, you get a turn-key Linux computer that’s ready and able to handle whatever AI code you throw at it. But if you’re trying to set up a lab for 30 students, the cost of even relatively affordable development boards can really add up.

Spoiler: These things don’t exist.

Which is why [Tea Vui Huang] has developed jetson-emulator. This Python library provides a work-alike environment to NVIDIA’s own “Hello AI World” tutorials designed for the Jetson family of devices, with one big difference: you don’t need the actual hardware. In fact, it doesn’t matter what kind of computer you’ve got; with this library, anything that can run Python 3.7.9 or better can take you through NVIDIA’s getting started tutorial.

So what’s the trick? Well, if you haven’t guessed already, it’s all fake. Obviously it can’t actually run GPU-accelerated code without a GPU, so the library [Tea] has developed simply pretends. It provides virtual images and even “live” camera feeds to which randomly generated objects have been assigned.

The original NVIDIA functions have been rewritten to work with these feeds, so when you call something like net.Classify(img) against one of them you’ll get a report of what faux objects were detected. The output will look just like it would if you were running on a real Jetson, down to providing fictitious dimensions and positions for the bounding boxes.

If you’re a hacker looking to dive into machine learning and computer vision, you’d be better off getting a $59 Jetson Nano and a webcam. But if you’re putting together a workshop that shows a dozen people the basics of NVIDIA’s AI workflow, jetson-emulator will allow everyone in attendance to run code and get results back regardless of what they’ve got under the hood.

NVIDIA Announces $59 Jetson Nano 2GB, A Single Board Computer With Makers In Mind

October 5, 2020 by 47 Comments

NVIDIA kicked off their line of GPU-accelerated single board computers back in 2014 with the Jetson TK1, a $200 USD development system for those looking to get involved with the burgeoning world of so-called “edge computing”. It was designed to put high performance computing in a small and energy efficient enough package that it could be integrated directly into products, rather than connecting to a data center half-way across the world.

The TK1 was an impressive piece of hardware, but not something the hacker and maker community was necessarily interested in. For one thing, it was fairly expensive. But perhaps more importantly, it was clearly geared more towards industry types than consumers. We did see the occasional project using the TK1 and the subsequent TX1 and TX2 boards, but they were few and far between.

Then came the Jetson Nano. Its 128 core Maxwell CPU still packed plenty of power and was fully compatible with NVIDIA’s CUDA architecture, but its smaller size and $99 price tag made it far more attractive for hobbyists. According to the company’s own figures, the number of active Jetson developers has more than tripled since the Nano’s introduction in March of 2019. With the platform accessible to a larger and more diverse group of users, new and innovative applications for machine learning started pouring in.

Cutting the price of the entry level Jetson hardware in half was clearly a step in the right direction, but NVIDIA wanted to bring even more developers into the fray. So why not see if lightning can strike twice? Today they’ve officially announced that the new Jetson Nano 2GB will go on sale later this month for just $59. Let’s take a close look at this new iteration of the Nano to see what’s changed (and what hasn’t) from last year’s model.

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