> You need to create a quantized TensorFlow Lite model and then compile the model for compatibility with the Edge TPU. We will provide a cloud-based compiler tool that accepts your .tflite file and returns a version that's compatible with the Edge TPU.
This seems like a new low in software freedom, and pretty risky to depend on as Google is known to shutter services pretty often and could just decide to turn off their cloud-based compiler at any time they feel like.
I went to their shing dig and they were working their butt off to wow the developers who were invited. When I asked for hard number they were very mum about that and very evasive.
The timeline for Nervana chip have been always seemingly in this mystical horizon that is never solidified to a real date but over yonder.
Google is going to pull this crap? They got better software expertise than Intel though they may be able to do it. But after that fiasco with Angular 1 to 2 I wouldn't trust Google with any early version number.
This is the problem with certain kinds of technology that are bumping up against the edge of innovation. They're too powerful and if these technologies get in the hands of the DIY set, governments will lose control so they have to DRM and regulate everything. Heck, it's a problem with old technology. Many weapons aren't that complicated technologically, but their production and use are tightly regulated.
Edit: I'm not saying this is a good thing, I'm just deconstructing their though process for tight control over AI tech going forward.
For some reason drones are perceived to be completely different from all weapons that have existed before them. Those killer drones have existed for half a century. They are called missiles. Also the reason why UAV based fighter jets are not viable is because a cruise missile can be launched from 1000 miles away and for the cost of a global hawk you can send out more than a hundred of them.
If terrorists have access to explosives then it doesn't matter how they deliver them because most lucrative targets (= lots of people in a small area) are stationary or predictable. A simple bagpack filled with explosives was more than enough to injure hundreds of people during the Boston Marathon.
The "right thing to do" is to open up these technologies, so that everyone can harness its power, not hide them under the wing and discretion of the (already too) powerful.
[UPDATE] I misread and assumed the previous case (where no cloud tool was required) was still true (I worked with previous versions of this device).
The way I read the quote, you use TF-Lite to produce a quantized TF-Lite model, and then use a cloud based compiler to compile it for the actual chip.
This is why I asked "am I missing something." Do you have a reference for where the compiler exists in the open source TensorFlow project?
Mostly, what I'm interested in is learning what capabilities their TPU provides, to see if it would be useful for other similar kinds of kernels like DSP (which, like machine learning kernels, also involves a lot of convolution).
So I'm interested in looking at what the capabilities of the chip are, seeing what could be compiled to it. But I haven't found those docs, or found a compiler that could be studied. But maybe I'm not looking in the right place.
Here's an overview of the architecture of their Cloud TPUs, which has some good architectural details but doesn't documet the instruction set: