It seems like it would be extremely fast to me. Take a 50x50 block of pixels and expand those across a 100x100 pixel grid leaving blank pixels were you have missing data. If a blank pixel is surrounded by blue pixels, the probability of the missing pixel being blue is fairly high, I would assume.
That is a problem that is perfect for AI, actually. There is an actual algorithm that can be used for upscaling, but at its core, its likely boiled down to a single function and AI’s are excellent for replicating the output of basic functions. It’s not a perfect result, but it’s tolerable.
If this example is correct or not for FSR, I have no clue. However, having AI shit out data based on a probability is mostly what they do.
I am curious as to why they would offload any AI tasks to another chip? I just did a super quick search for upscaling models on GitHub (https://github.com/marcan/cl-waifu2x/tree/master/models) and they are tiny as far as AI models go.
Its the rendering bit that takes all the complex maths, and if that is reduced, that would leave plenty of room for running a baby AI. Granted, the method I linked to was only doing 29k pixels per second, but they said they weren’t GPU optimized. (FSR4 is going to be fully GPU optimized, I am sure of it.)
If the rendered image is only 85% of a 4k image, that’s ~1.2 million pixels that need to be computed and it still seems plausible to keep everything on the GPU.
With all of that blurted out, is FSR4 AI going to be offloaded to something else? It seems like there would be a significant technical challenges in creating another data bus that would also have to sync with memory and the GPU for offloading AI compute at speeds that didn’t risk create additional lag. (I am just hypothesizing, btw.)