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can someone explain to me why I want ML specific processor features or chips in my phone? I thought ML required massive amounts of data to be taught, most of

can someone explain to me why I want ML specific processor features or chips in my phone? I thought ML required massive amounts of data to be taught, most of which makes more sense in the cloud. Am I way off here?

jaydj 7y ago
Check out federated learning. https://medium.com/tensorflow/introducing-tensorflow-federated-a4147aa20041 https://medium.com/tensorflow/introducing-tensorflow-federat...
jopsen 7y ago
From what little I understand models are built in the cloud, compressed and evaluated on your phone. Better hardware probably means less power drain and larger models. There was some cool stuff about this in Google I/O keynote.
btown 7y ago
Training a model does require massive data and compute, but evaluating/using an already model (e.g. running a Hot Dog/Not Hot Dog classifier) can be done on mobile hardware. Accelerating this could, for instance, allow this to run in real time on a video feed.
NegatioN 7y ago
It doesn't have to be for training the models. You can run versions of trained models locally on your phone. Having a dedicated chip will allow for more snappy calculation of those fancy snapchat filters, language translation, image recognition etc
joshvm 7y ago
Aside from the runtime difference between training and inference, having on-device ML makes a lot of sense for other reasons. There can be more guarantees over data privacy, since your data can stay on-device. It also reduces bandwidth as there's no need to upload data for classification to the cloud. And that also may mean it's faster, potentially real time, since you don't have that round trip latency. This is not necessarily for phones. Lots of (virtually all?) low power IoT devices have ARM cores. There are plenty of environments where the cloud or compute power isn't available.
Traster 7y ago
Machine learning is like any learning, there's the learning stage and the putting it into practice. ML in the cloud is like researchers coming up with a new way to slice bread, ML on your phone is like the your local baker following the instructions in a new cook book. You still need a skilled baker to follow the instructions.
mruts 7y ago
The way neural networks work is that each perceptron runs a linear equation of the form: w1x1 + w2x2...wnxn. Even if you train the model somewhere else, you still need to hold the weights locally in each perceptron in order to evaluate the model. This requires hardware in which you can multiply these huge matrices quickly, even if the weights are downloaded from the cloud.
mensetmanusman 7y ago
E.g. accurate edge detection algorithms are better done with ML-based mathematics and accompanying optimized transistor layouts, this would enable better augmented reality type graphic additions from things like snapchat selfies to visualizing free-space CAD.

jaydj 7y ago

Check out federated learning. https://medium.com/tensorflow/introducing-tensorflow-federated-a4147aa20041 https://medium.com/tensorflow/introducing-tensorflow-federat...

jopsen 7y ago

From what little I understand models are built in the cloud, compressed and evaluated on your phone. Better hardware probably means less power drain and larger models. There was some cool stuff about this in Google I/O keynote.

btown 7y ago

Training a model does require massive data and compute, but evaluating/using an already model (e.g. running a Hot Dog/Not Hot Dog classifier) can be done on mobile hardware. Accelerating this could, for instance, allow this to run in real time on a video feed.

NegatioN 7y ago

It doesn't have to be for training the models. You can run versions of trained models locally on your phone. Having a dedicated chip will allow for more snappy calculation of those fancy snapchat filters, language translation, image recognition etc

joshvm 7y ago

Aside from the runtime difference between training and inference, having on-device ML makes a lot of sense for other reasons. There can be more guarantees over data privacy, since your data can stay on-device. It also reduces bandwidth as there's no need to upload data for classification to the cloud. And that also may mean it's faster, potentially real time, since you don't have that round trip latency. This is not necessarily for phones. Lots of (virtually all?) low power IoT devices have ARM cores. There are plenty of environments where the cloud or compute power isn't available.

Traster 7y ago

Machine learning is like any learning, there's the learning stage and the putting it into practice. ML in the cloud is like researchers coming up with a new way to slice bread, ML on your phone is like the your local baker following the instructions in a new cook book. You still need a skilled baker to follow the instructions.

mruts 7y ago

The way neural networks work is that each perceptron runs a linear equation of the form: w1x1 + w2x2...wnxn. Even if you train the model somewhere else, you still need to hold the weights locally in each perceptron in order to evaluate the model. This requires hardware in which you can multiply these huge matrices quickly, even if the weights are downloaded from the cloud.

mensetmanusman 7y ago

E.g. accurate edge detection algorithms are better done with ML-based mathematics and accompanying optimized transistor layouts, this would enable better augmented reality type graphic additions from things like snapchat selfies to visualizing free-space CAD.