NVIDIA Unveils Jetson AGX Orin Developer Kit

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.

Author with book

Learn All About Writing A Published Technical Book, From Idea To Print

Ever wondered what, exactly, goes into creating a technical book? If you’d like to know the steps that bring a book from idea to publication, [Sara Robinson] tells all about it as she explains what went into co-authoring O’Reilly’s Machine Learning Design Patterns.

Her post was written in 2020, but don’t let that worry you, because her writeup isn’t about the book itself so much as it is about the whole book-writing process, and her experiences in going through it. (By the way, every O’Reilly book has a distinctive animal on the cover, and we learned from [Sara] that choosing the cover animal is a slightly mysterious process, and is not done by the authors.)

It turns out that there are quite a few steps that need to happen — like proposals and approvals — before the real writing even starts. The book writing itself is a process, and like most processes to which one is new, things start out slow and inefficient before they improve.

[Sara] also talks a bit about burnout, and her advice on dealing with it is as insightful as it is practical: begin by communicating honestly how you are feeling to the people involved.

Over the years I’ve learned that people will very rarely guess how you’re feeling and it’s almost always better to tell them […] I decided to tell my co-authors and my manager that I was burnt out. This went better than expected.

There is a lot of code in the book, and it has its own associated GitHub repository should you wish to check some of it out.

By the way, [Sara] celebrated publication by making a custom cake, which you can see near the bottom of her blog post. This comes as no surprise seeing as she has previously managed to combine machine learning with her love of making cakes!

AI Maybe Revives Dead Languages

While Star Trek’s transporter is hard to imagine — perfect matter movement across vast distances with no equipment on one end — it may not be the most far-fetched piece of tech on the Enterprise. While there are several contenders, I strongly suspect the universal translator is the most unlikely MacGuffin. After all, how would you decipher a totally unknown language in real-time? Of course, no one wants to watch 30 episodes of TV about how we finally figured out what Klingons call clouds, so pretty much every science fiction movie has some hand-waving explanation for speaking the viewer’s language. Farscape had microbes, some aliens have telepathy that works with alien brains of any kind, and still others study English from afar for decades off camera. Babelfish anyone?

I was thinking about this because of an article I read by [Alizeh Kohari] about [Jiaming Luo’s] work using AI to decode dead languages. While this might seem to be similar to Spock’s translator, it really isn’t. Human languages change over time and distance. You only have to watch the BBC or read something written by Thomas Jefferson to see that. But there is still a lot in common, at least within certain domains.

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