If Scarlett is designed with the cloud in mind, are they designing it to be a very capable machine learning device they can leverage for other cloud computing applications? And could they be sacrificing too much die space and rasterization performance giving PS5 the rasterization/traditional performance crown?
There’s a lot to unpack there; so I’m
A bit biased when it comes to ML since I now work in ML and AI.
Firstly, tensor cores aren’t the be all end all for fast processing. it doesn’t solve bandwidth or cache issues, and we see this issue with RTX lines vs the Pascal ones. More bandwidth but less cache and some algorithms will still run better on Pascal because of cache size.
It’s not enough to have tensor cores, some algorithms will be bandwidth bound, others compute etc. So it is not so straight forward to just dump in tensor processing units and call it a day.
That being said; people need to understand that the addition of AI makes for some really crazy stuff. Yes, raw compute will be down, frame rates could be capped as well since neural networks will always roughly run at the same speed, the whole network must completely run to deliver an output. But the purpose of AI and ML is to arrive at conclusions that using standard programming cannot; too many rules; too many states or too many permutations. The best way to look at 'why AI' vs traditional programming; the niche for AI is to solve problems that are computationally too taxing or impossible to code that many scenarios for (too dynamic, or requiring the need for intelligence to break out of a standard ruleset. If the purpose of traditional compute is to reduce the workload as much as possible so that it's computable quickly (and in some manner in which you can keep the code manageable and sustainable), ML AI is about tackling workloads that are not computable or unmanageable.
One could easily make the argument that you can go without AI, and that's fine. But having options to solve problems with 2 completely separate methods should help provide some wins that would normally be restricted by the power of the hardware or solve problems that require constant adaption.
a game studio that embraces AI will have a major lead over other studios as they continue down the journey of ML and AI and having the hardware available to be used for multiple purposes makes it useful to examine its usage in more areas than just graphics denoising.
Considering how long a generation is, Ill take detuned hardware for AI support.
