4 ms·

I read the entire blog post and I don't understand the fanfare, it's a custom SoC, presumably to rival Apple's M1. I don't see anything innovative about this,

I read the entire blog post and I don't understand the fanfare, it's a custom SoC, presumably to rival Apple's M1. I don't see anything innovative about this, besides heralding "begun, the neural net wars have". What is the benefit of having all of the ML bits on device? Can models leverage them post training?

dman 5y ago
Hopefully this translates into the phones being supported for longer since they are an inhouse design.
slownews45 5y ago
Latency can be better if they get it right.
hn_throwaway_99 5y ago
> What is the benefit of having all of the ML bits on device? Can models leverage them post training? Yes, the whole point is to be able do to things like improve personalized speech recognition on-device, image recognition on-device, translation on-device, etc.
Jtsummers 5y ago
Reduced latency. Which has an apparent performance boost versus sending data to Google for processing and waiting for a response. Potentially improved privacy (this is how Apple tries to sell it). Less data has to leave the phone to gain the utility of the ML models. Improved device performance. Reduced network use and better specialized chips leading to better performance. Either in terms of better battery life or better time to get the result.
- jmnicolas 5y ago
  > Potentially improved privacy On a Google device I would say the potential is reduced to zero!
  - npteljes 5y ago
    I agree that the prospect is ludicrous. Hoping for potential of privacy from an ad-tech giant. Nonsense.
bitwise-evan 5y ago
Are you asking what is the advantage of having ML optimized hardware on device? Yes, running inferences is expensive too, especially for video, photo, and speech processing. I would expect this phone to have user noticeable improvements in those three areas.
pdpi 5y ago
> Can models leverage them post training? Yes. The whole point is that you have a slow process train the model offline on very large volumes of sample data, then use that trained model to make actual inferences based on data you find in the field. As those models become bigger and more complex, it takes progressively more computing power to run inference on those models. These ML accelerators are effectively the new GPUs — highly specialised processors designed to more efficiently handle highly specialised workloads.
xnx 5y ago
To consumers they have to promote the magic/wondrous chip, but they greatest benefit to Google is controlling more of the hardware stack themselves.
paxys 5y ago
There is a huge benefit to being able to do common tasks like speech recognition and photo processing fully on device without a server round trip.
nharada 5y ago
Latency, inference cost to Google. Privacy, in theory

dman 5y ago

Hopefully this translates into the phones being supported for longer since they are an inhouse design.

slownews45 5y ago

Latency can be better if they get it right.

hn_throwaway_99 5y ago

> What is the benefit of having all of the ML bits on device? Can models leverage them post training? Yes, the whole point is to be able do to things like improve personalized speech recognition on-device, image recognition on-device, translation on-device, etc.

Jtsummers 5y ago

Reduced latency. Which has an apparent performance boost versus sending data to Google for processing and waiting for a response. Potentially improved privacy (this is how Apple tries to sell it). Less data has to leave the phone to gain the utility of the ML models. Improved device performance. Reduced network use and better specialized chips leading to better performance. Either in terms of better battery life or better time to get the result.

jmnicolas 5y ago
> Potentially improved privacy On a Google device I would say the potential is reduced to zero!
- npteljes 5y ago
  I agree that the prospect is ludicrous. Hoping for potential of privacy from an ad-tech giant. Nonsense.

bitwise-evan 5y ago

Are you asking what is the advantage of having ML optimized hardware on device? Yes, running inferences is expensive too, especially for video, photo, and speech processing. I would expect this phone to have user noticeable improvements in those three areas.

pdpi 5y ago

> Can models leverage them post training? Yes. The whole point is that you have a slow process train the model offline on very large volumes of sample data, then use that trained model to make actual inferences based on data you find in the field. As those models become bigger and more complex, it takes progressively more computing power to run inference on those models. These ML accelerators are effectively the new GPUs — highly specialised processors designed to more efficiently handle highly specialised workloads.

xnx 5y ago

To consumers they have to promote the magic/wondrous chip, but they greatest benefit to Google is controlling more of the hardware stack themselves.

paxys 5y ago

There is a huge benefit to being able to do common tasks like speech recognition and photo processing fully on device without a server round trip.

nharada 5y ago

Latency, inference cost to Google. Privacy, in theory