Wouldn’t this absolutely hammer the battery though, or at least give the CPU a hard time? My understanding is that offloading the work to a cloud platform means that the processor-intensive inputting, parsing, generating, and outputting operations are done in purpose-built datacentres, and end user devices just receive the prepared answer.
Wouldn’t this rinse the battery and increase the overall device temperature for “normal” end users?
Fair warning: I haven’t read the two papers outlined in the article.
Apple already does a lot of this stuff. For example, it’ll do offline face recognition for your photos while your phone is charging overnight.
Plus, Apple is ahead of the curve when it comes to performance on this stuff. You don’t want to be running Stable Diffusion on your iPhone, but smaller AI is perfectly fine. Plus, unlike on Android, there are huge amounts of devices with ML accelerator chips that can run these models efficiently, allowing for power consumption optimisations by not having to provide a CPU fallback.
We’ll have to see how effective this will be in practice, but Apple generally doesn’t bring these types of features to their newer devices until they’re ready for daily use.
It’s a technical challenge but I wouldn’t rule it out. Apple has been using a “neural engine” in their SoC for faced id, etc. for a while. So it’s something they’ve been working on. It will need to get better, but AI models are also getting more efficient.
CPUs can have special hardware accelerators for stuff like this, and you’d be surprised how powerful our little phone CPUs are and how optimized stuff like this can become.
I’m showing my age here, but much like we had math coprocessors running beside the 286 and 386 gen CPUs to take on floating point operations; then graphics cards offloaded geometry-based math operations to GPU’s - are we looking at AI-style die or chips to specifically work on AI functions?
Excuse my oversimplification, this isn’t my field of expertise!
Wouldn’t this absolutely hammer the battery though, or at least give the CPU a hard time? My understanding is that offloading the work to a cloud platform means that the processor-intensive inputting, parsing, generating, and outputting operations are done in purpose-built datacentres, and end user devices just receive the prepared answer.
Wouldn’t this rinse the battery and increase the overall device temperature for “normal” end users?
Fair warning: I haven’t read the two papers outlined in the article.
Apple already does a lot of this stuff. For example, it’ll do offline face recognition for your photos while your phone is charging overnight.
Plus, Apple is ahead of the curve when it comes to performance on this stuff. You don’t want to be running Stable Diffusion on your iPhone, but smaller AI is perfectly fine. Plus, unlike on Android, there are huge amounts of devices with ML accelerator chips that can run these models efficiently, allowing for power consumption optimisations by not having to provide a CPU fallback.
We’ll have to see how effective this will be in practice, but Apple generally doesn’t bring these types of features to their newer devices until they’re ready for daily use.
It’s a technical challenge but I wouldn’t rule it out. Apple has been using a “neural engine” in their SoC for faced id, etc. for a while. So it’s something they’ve been working on. It will need to get better, but AI models are also getting more efficient.
If the scope of “Ai” isn’t wide, I’d imagine the battery and cpu usage would be minimized.
CPUs can have special hardware accelerators for stuff like this, and you’d be surprised how powerful our little phone CPUs are and how optimized stuff like this can become.
Awesome, thanks for the insight.
I’m showing my age here, but much like we had math coprocessors running beside the 286 and 386 gen CPUs to take on floating point operations; then graphics cards offloaded geometry-based math operations to GPU’s - are we looking at AI-style die or chips to specifically work on AI functions?
Excuse my oversimplification, this isn’t my field of expertise!
not a dedicated chip per se, the trend is to build it directly into the SoC (mobile devices) or the dedicated GPU
Apple added (a while back) what they call a “Neural Engine,” which is hardware dedicated to efficient execution of ML workloads.
https://en.m.wikipedia.org/wiki/Apple_A11
They have been refining it ever since. I would not be surprised if they made advancements in both the hardware and software used for local GAI.
And Google did the same with the Tensor Processor Unit in the Pixels.