Re:Paper review: TensorFlow, Machine Learning on Heterogeneo...
by krasin on Monday January 11, 2016 @05:43PM
(#10884165)
Another supportive (for your point) evidence is the Tensorflow Android example: https://github.com/tensorflow/tensorflow/tree/master/tensorf...
It runs the network locally on the device. It's fast enough.
Re:Paper review: TensorFlow, Machine Learning on Heterogeneo...
by dgacmu on Tuesday January 12, 2016 @01:04AM
(#10885817)
"Sometimes, a cigar is just a cigar."
Phones are increasingly powerful, prevalent, and for a lot of people, their primary computer. Being able to use the same code, data, and test cases for every platform you want to train or run your model on is good from a software engineering perspective. Don't underestimate the challenges of software eng. when ML is involved -- see, for example, http://icml.cc/2015/invited/LeonBottouICML2015.pdf
Re:Paper review: TensorFlow, Machine Learning on Heterogeneo...
by srean on Tuesday January 12, 2016 @05:43AM
(#10886599)
Where can I know more about Maui that OP speaks of ?
Maui is a system for offloading of smartphone code execution onto backend servers at method-granularity. The system relies on the ability of managed code environment (.NET CLR) to be run on different platforms. By introducing this automatic offloading framework, Maui enables applications that exceed memory/computation limits to run on smartphones in a battery- & bandwidth-efficient manner.
TensorFlow enables cloud backend support for machine learning to the private/device-level machine learning going on in your smartphone. It doesn't make sense for a power-hungry entire TensorFlow program to run on your wimpy smartphone. Your smartphone will be running only certain TensorFlow nodes and modules, the rest of the TensorFlow graph will be running on the Google cloud backend. Such a setup is also great for preserving privacy of your phone while still enabling machine learned insights on your Android.
I don't see any real evidence of this in the paper, code or examples. Certainly running it it doesn't feel like it is designed to operate over high-latency connections like that.
Instead, I think TensorFlow is deigned to work on mobile so that it is comparatively easy for a pre-trained model to be deployed and run on a constrained mobile environment.
That's what Google did for the Android voice recognition model (although that was pre-TensorFlow), and they have released a pre-trained image classification model that runs on TensorFlow.