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.
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.
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.
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.
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.
An "NPU" is a matrix multiplier accelerator. It removes some general-purpose stuff that GPUs provide in favor of more "AI"-useful units, like support for values a byte or smaller (i.e., FP4, INT4, etc.).
From what I can tell, NPUs are mostly being used by Microsoft to encourage vendor lock-in to the MicrosoftML/ONNX platform (similar to their DirectX playbook).
They're used a lot on mobile. Apple uses their "neural engine" NPU to power their on-device ML stuff and Samsung does something similar in their Exynos processors. Apple also exposes the NPU to developers via CoreML.
"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.
The marketing speak isn't exactly clear, but I believe the point is that this is like an NPU inside of the GPU instead of next to it as a separate device. That would indeed be new, and I can see how it'd be beneficial to integration with games.
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.
> Imagine a game with bare-bones graphics and lighting
https://eu-images.contentstack.com/v3/assets/blt740a130ae3c5...
# The Art Of Braid: Creating A Visual Identity For An Unusual Game
https://www.gamedeveloper.com/design/the-art-of-braid-creati...
I don't think you have a realistic view of how this will be used.
First, porn.
Second, artificial botting to make your game look active.
Third, hire a art developer in india, VPN them to your AI tool, fire them when the game is done.
You really should check your prescription rose colored glasses.
So? Why would that stop us from doing cool things for games?
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.
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's a copy of DLSS/FSR4 which are pretty well understood by now. As for the schedule, Arm always announces IP ahead of time.
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.
Yes
There are now at least three ways to accelerate machine learning models on consumer hardware:
And of course models can also be run, without acceleration, on the CPU.An "NPU" is a matrix multiplier accelerator. It removes some general-purpose stuff that GPUs provide in favor of more "AI"-useful units, like support for values a byte or smaller (i.e., FP4, INT4, etc.).
All three of them accelerate matrix multiplications actually.
any thing that computes matmul faster than by hand technically accelerates matmul - so what's your point?
At least for desktop gaming, the tensor cores are in the GPU compute units (SM), same as for the big data center GPUs.
It seems ARM believe it makes sense to go a different route for mobile gaming.
From what I can tell, NPUs are mostly being used by Microsoft to encourage vendor lock-in to the MicrosoftML/ONNX platform (similar to their DirectX playbook).
They're used a lot on mobile. Apple uses their "neural engine" NPU to power their on-device ML stuff and Samsung does something similar in their Exynos processors. Apple also exposes the NPU to developers via CoreML.
"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.
The marketing speak isn't exactly clear, but I believe the point is that this is like an NPU inside of the GPU instead of next to it as a separate device. That would indeed be new, and I can see how it'd be beneficial to integration with games.