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Comment by deepnotderp | Hacker News Reader

Exactly, but how many of those were good decisions?

Altera-- Not actually a bad decision, but it seems to have done nothing but bolstered Xilinx's position.

MobileEye-- A great way to burn ~$15 billion. The fact that Intel felt that it had to purchase that company with really no defensible advantage other than its maps highlights the huge challenges facing Intel if they're unable to cultivate an internal SDC team.

Movidius-- Legitimately I have no idea why they paid so much for a glorified DSP. But then again, so are most "Deep Learning Processors" right now.

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>> Movidius ⸺ Legitimately I have no idea why they paid so much for a glorified DSP. But then again, so are most "Deep Learning Processors" right now.

Curious, do you know alternative "DSPs" able to achieve similar results for Deep-Learning, Computer Vision or Machine-Learning algorithm acceleration?

I would highly appreciate a real answer, because I intended to buy one of these movidius "sticks". And further accelerate the Laptop with an eGPU.

I'm very biased here, don't ask me on that :)

That being said, I think pretty much everything I say (admittedly with some hyperbole) is backed up by data.

So I would recommend at the moment, for a shipping processor, get a GPU.

It's really hard to compare Movidius with anything else because there are so few published benchmarks.

A few notes though:

Movidius is inference only. That might be useful if you have the specific requirements that needs low power, high speed inference and also somehow has to have a x86 CPU.

If you want high speed, low power inference and don't need x86 then the NVidia Jetson wipes the floor with it.

If you want low speed (~2 inference/second), low power then RPi is a good option.

If you want high speed, you need GPU(s).

The Movidius Stick supports the Raspberry Pi now as a deployment (not development) option, so you can get low power and high speed inference on a single RPi. I have one of these on my desk and they're neat but I haven't done much with it yet. I did grab one because I had heard it would support RPis, though.

Granted, it's 2x the price of a Rpi 3. All together that's about $100 USD. And NVidia just announced the TX1 SE Devkit at $200 USD. I have a TX2, but the TX1 will definitely do better at a higher power/size profile.

The MCS only supports Caffe as well, while the TX1/TX2 will support a wider array of DL frameworks (as well as FP16 support since it's a Tegra).

Interesting. I didn't realize they would work on a RPi.

Movidius have promised TF support I think.

Thank you for the great answer Nick, that's the best summary I could've hoped for!

So in summary:

⇒ low power, high speed inference on x86 CPU 🡺 Intel Movidius

⇒ low power, high speed inference non x86 🡺 NV Jetson

⇒ low power, low speed inference 🡺 RPi and similar

⇒ high power, high speed inference 🡺 GPU(s)

NVidia Jetson is the obvious winner, if there is no viable alternative, however Jetson is quite expensive and therefore I can't go that route. I want to speed up training & inference. eGPUs are more or less affordable, but low power training & inference at medium or low-cost would strike me as a clear winner.

Sorry for the late answer, nonetheless, even though I didn't get an alternative DSP that offers similar advantages as Movidius, I'm still grateful for your insightful comment.

Yes, GPUs (or cloud) are the way to go at the moment if you have any training and want it quick.

HOWEVER, If you are careful, for some models you can get cost benefits by training on cloud CPUs. See http://minimaxir.com/2017/07/cpu-or-gpu/

Also, what's your use case? That differs significantly, because IIRC, the Movidius chip only supports inference, so if you want to train models too, then that's a non-starter.

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