I think ML has lots of potential in this area specifically.

Imagine a game with bare-bones graphics and lighting, and a NN that converts it into something pretty. Indie developers can make AA-looking games and all game developers can devote more effort into design and logic. Artists will still be needed for art direction and possibly fine-tuning, although there will be less needed for each game (also less developers needed with AI agents and better tools).

Related, ML also has potential for AI enemies (and allies). Lots of players still prefer multiplayer, in part because humans are more realistic enemies (but also because they want to beat real humans); but multiplayer games struggle because good netcode is nontrivial, servers are expensive, some players are obnoxious, and most games don’t have a consistent enough playerbase.

This article 2 links deep had better technical details -

https://community.arm.com/arm-community-blogs/b/mobile-graph...

Upscaling solution mainly targeted at mobile gaming, with an 'AI pipeline' for upscaling graphics (They claim 540p upscaled to 1080p at 4ms per frame). I'm a bit skeptical because this is a press release for chips that are in the works and claim to be releasing in DEC-26, and then on actual devices after that. So sounds more like a strategic/political move (Perhaps stock price related manoeuvring).

Unreal Engine 5 plugin will allow previewing the upscaled effects using the though, which will be nice for game developers.

It sounds like this a geared towards games. However, I like the idea of exposing all of the ML features through Vulkan extensions rather than some proprietary API. Though I think exposing them through OpenCL extensions would work for me as well.

At this point, IMG / PowerVR isn't even used by MediaTek. Which means GPU on Mobile is just Apple, Qualcomm Adreno, ARM Mali. Still wish ARM had rebranded their Mali range.

Samsung Exynos uses AMD RDNA but I am not even sure if they are being used at all. Nvidia seems to have no interest in the market.

I figured there is a need for generating a lot of samples and building a predictive model per game for best results. Documentation confirms:

> Most of these corner cases can be resolved by providing the model with enough training data without increase the complexity and cost of the technique. This also enables game developers to train the neural upscalers with their content, resulting in a completely customized solution fine-tuned for the gameplay, performance, or art direction needs of a particular title.

Source: https://developer.arm.com/documentation/111019/latest/

hardware-wise, this seems like a NVIDIA TensorCore? via https://huggingface.co/Arm/neural-super-sampling/blob/main/2...

- https://github.com/KhronosGroup/Vulkan-Docs/blob/5d386163f25... Adding tensor ops to the shader kernel vocaborary (SPIR-V). Promising.

- https://github.com/KhronosGroup/Vulkan-Docs/blob/5d386163f25... Adding TenforFlow/NNAPI/-like graph API. Good luck.

There are now at least three ways to accelerate machine learning models on consumer hardware:

  - GPU compute units (used for LLMs)
  - GPU "neural accelerators"/"tensor cores" etc (used for video game anti-aliasing and increasing resolution or frame rate)
  - NPUs (not sure what they are actually used for)

And of course models can also be run, without acceleration, on the CPU.

How many TOPS/WHr?

"Arm neural technology is an industry first, adding dedicated neural accelerators to Arm GPUs"

HiSilicon Kirin 970 had an NPU in like 2017. I think almost every performance-oriented Arm chip released in the last 5 years has had some kind of NPU on it.

I suspect they are using Arm here to mean "Arm-the-company-and-brand" not "Arm the architecture", which is both misleading and makes the claim completely meaningless.