Google Coral Edge TPU
coral.withgoogle.com
coral.withgoogle.com
I am retiring in a couple off weeks from my job managing a machine learning team and I intend on being a ‘gentleman scientist’ studying things of interest, without worrying about immediate practicality. Of most interest is local ML using tensorflow.js and devices like the Edge TPU, and also hybrid symbolic AI and deep neural net systems.
Anyway, good to see competition for edge devices.
> It should take about one minute for compilation to complete.
...also, it should take about six months for Google to lose interest in this product, at which point the product you made when you integrated the Edge TPU -- is stuck without updates.
Can you show me statistically that Google is any more likely to discontinue something than any other startup? Or than Apple or Amazon?
A few people got upset about Google discontinuing Reader, but that was a looong time ago. And they've certainly discontinued other things to... but just like every other company.
They seem to discontinue a lot of products, including ones with fairly large user bases. It seems like a valid concern if you're going to try to build something on top of their stuff.
Disclaimer: I work at Google.
This however seems to be a product with no SLA and no guarantees, outside of the cloud offerings etc. I kinda agree with OP, Google's track record is bad when it comes to this kind of products.
And yes I think they are worse than other companies. Google isn't a hardware company, so they're worse than apple in that regard. And Amazon would do it through AWS, which would also make this fall inside their core competency.
It's a major issue for actual deployments of hardware in e.g. medical, education, research settings where a machine may end up supporting a piece of machinery for a couple decades on no support but just some spare duplicate parts that can be swapped in.
I once used a fiber optic splicer at MIT that was 2.5 decades old and ran DOS. Nobody gave a crap that it was DOS. We just needed fibers spliced and a new shiny touch screen splicer would cost $30K.
[1]: https://killedbygoogle.com
[2]: https://gcemetery.co
They mentioned previously that you had to compile your models on the cloud, and not locally on your computer. Not sure if they've changed this policy.
The proprietary compiler thing sucks, but it is where a lot of the secret sauce is, unfortunately. But a binary wouldn't be too much to ask for...
Wow, I was interested in this, right up until I read that. Talk about "weak sauce".
Sorry Google, but no, I will not use your proprietary compiler, especially when it's only available in the cloud, and become beholden to hardware which could instantly become a very expensive paperweight when you shut down the compiler service. No f'in way.
Release an open source compiler and I'm on-board. Otherwise, stuff it.
The edge TPU can do MobileNet V2 at 100 FPS.
An iPhone 7 can do it at 145 FPS (source https://machinethink.net/blog/mobilenet-v2/)
The deal is though that the Edge TPU is able to do it at much lower power.
Meaning, if I have an application that needs a big hot PCI-E card attached to a physical server I own somewhere, comparable to GPUs now, the TPU is not for me. But meanwhile, a bunch of NN research and frameworks on top of TensorFlow will treat these proprietary things as a first class citizen.
This would specifically let you make sure that the TensorFlow ops your algorithms use are supported on a TPU.
http://linuxgizmos.com/google-launches-i-mx8m-dev-board-with...
E: Hah, seems like my topic got merged with this one. Interesting how I was short from OPs post by like a minute a two. Such a coincidence!
On that website, each page has the Google logo at the bottom and "Copyright 2019 Google LLC. All rights reserved.". Also, at [1], Google LLC is mentioned as manufacturer of the devices. At this point, Coral still seems to be a brand only, not a company. Maybe they just didn't want to harm/affect their "main" trademark with this. Or they actually do want to create a separate company and this is the first step.
M4 application notes (http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc....) says the M{-0..4} doesn't have any internal cache, but that it can be provided by the SoC. Presumably that's what's happening here -- although it seems weird that this can be called an L1 Cache (although I'm by no means an expert on this so can't really comment!).
I'm not in the space per-say but what are the predominant OS choices for ML/AI Devs?
However I really wish they would make something beefier, to compete with e.g. Nvidia's Xavier.
How about the Edge TPU specifications? Did I overlook those too?