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Comment by nl | Hacker News Reader

Cheaper than the $150 Google TPU Dev Board, and looks like it can do training as well as inference.

Training is a slight win (although it's going to be too slow for anything useful really).

But it looks like the TPU will outperform this somewhat for inference. The 256 core Jetson (this has 128 cores) could run MobileNet-v2 at between 12 and 20 ms per image (depending on batch size)[1], while the USB TPU adapter takes 2.3ms per image [2]

Also, doesn't require you to send your model to their company.

Nor does the TPU dev board.

[1] https://arxiv.org/pdf/1810.00736.pdf (see table 1)

[2] https://coral.withgoogle.com/tutorials/edgetpu-faq/

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>> Nor does the TPU dev board.

Yes it does require you to send your model to Google: https://coral.withgoogle.com/web-compiler/

Oh, interesting! I hadn't seen that.

I suspect it's not going to be like that forever, although I could be wrong. No self respecting AI shop will send them anything production-grade if that's how they want to play it. It'll all be hobbyists and bullshit projects, which, I suspect, is not the clientele they want to attact if it's even a somewhat serious cloud/IoT play. Although this is Google, so it could be canceled in 6 months, too, after the project managers and TLs get their promotions.

Google should license this thing to someone else who can make it in good quantity and sell it really cheap, so others build it into their designs. I'd be pretty excited if that happened.

And you have to check a box that says "I agree that my model will only be used for applications that follow Google's AI Principles." Wow.

Which may or may not include automatically killing people via drones.

They're pretty clear that that isn't kosher:

Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people.

https://www.blog.google/technology/ai/ai-principles/

But drones that spy on people are ok as long as they aren't "violating internationally accepted norms" which sort of sounds like a blank check.

Wouldn't this depend on the definition of "injury"? You could argue that under some definitions of "injury" Google violates its own "AI principles" already.

Seems to lose in Mobilenet and win in other benches according to Nvidia's benchmarks.

I'll wait for independent testing before I drop $100.

https://devblogs.nvidia.com/jetson-nano-ai-computing/

Horseshit in that bench right off the bat: I have a Google Edge TPU board right in front of me and its perf on SSD300 is 70fps, not 48. That's with the browser demo, which (as far as I can tell) includes realtime encoding of h264 for streaming. Almost twice as much as Jetson, and likely in a much more modest power envelope. NVIDIA is known for dishonesty in their benchmarks. Although TPU is, of course, a quantized play, and Maxwell will really suck for that, unless it's been tweaked specifically for this board.

OTOH, fp32 models are _much_ easier to work with, and this thing has more RAM so you can waste it on 32 bit weights, and NVIDIA's software toolkit is second to none. So the Jetson looks pretty tempting as well. I just wish they didn't try to insult my intelligence.

Oh yeah - I never said to trust their benches. Take OEM benches especially vs competitors with the grainiest grains of salt.

When people start getting their hands on them I'll start seeing independent benches, and I think anandtech got their hands on one. Hopefully soon™.

Interesting. I hadn't seen that, but the NVidia numbers on their own products seem credible. I do agree that the flexibility of having real CUDA cores is nice.

couldn't this play games then? seems to be a pretty nice platform for a homebrew videogame console.

definitely could. retroarch would run great under linux, and a wide variety of cores are available.

but im more interested as a cudann box

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