He specifically cited bad battery life on the ROG Ally and Lenovo Go, saying that getting only one hour of battery life isn’t enough. The Steam Deck (especially the OLED model) does a lot better battery wise, but improving power efficiency should really help with any games that are maxing out the Deck’s power.
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.)
The thing with “AI” or better still, ML cores, is that they’re very specialized. Apple hasn’t been slapping ML cores in all of their cpus since the iPhone 8 because they are super powerful, it’s because they can do some things (that the hardware would have no problem doing anyway) by sipping power. You don’t have to think about AI as in the requirements for huge LLM like ChatGPT that require data centers, think about it like a hardware video decoder: This thing could play easily 1080p video! Or, going with raw cpu power rather than hardware decoding, 480p. It’s why you can watch hours of videos on your phone, but try doing anything that hits the cpu and the battery melts.