I used to work at Intel until recently. Pat Gelsinger (the prior CEO) had made one of the top goals for 2024 the marketing of the "AI PC".

Every quarter he would have an all company meeting, and people would get to post questions on a site, and they would pick the top voted questions to answer.

I posted mine: "We're well into the year, and I still don't know what an AI PC is and why anyone would want it instead of a CPU+GPU combo. What is an AI PC and why should I want it?" I then pointed out that if a tech guy like me, along with all the other Intel employees I spoke to, cannot answer the basic questions, why would anyone out there want one?

It was one of the top voted questions and got asked. He answered factually, but it still wasn't clear why anyone would want one.

The problem is essentially memory bandiwdth afiak. Simplifying a lot in my reply, but most NPUs (all?) do not have faster memory bandwidth than the GPU. They were originally designed when ML models were megabytes not gigabytes. They have a small amount of very fast SRAM (4MB I want to say?). LLM models _do not_ fit into 4MB of SRAM :).

And LLM inference is heavily memory bandwidth bound (reading input tokens isn't though - so it _could_ be useful for this in theory, but usually on device prompts are very short).

So if you are memory bandwidth bound anyway and the NPU doesn't provide any speedup on that front, it's going to be no faster. But has loads of other gotchas so no real "SDK" format for them.

Note the idea isn't bad per se, it has real efficiencies when you do start getting compute bound (eg doing multiple parallel batches of inference at once), this is basically what TPUs do (but with far higher memory bandwidth).

In theory NPUs are a cheap, efficient alternative to the GPU for getting good speeds out of larger neural nets. In practice they're rarely used because for simple tasks like blurring, speech to text, noise cancellation, etc you can get usually do it on the CPU just fine. For power users doing really hefty stuff they usually have a GPU anyway so that gets used because it's typically much faster. That's exactly what happens with my AMD AI Max 395+ board. I thought maybe the GPU and NPU could work in parallel but memory limitations mean that's often slower than just using the GPU alone. I think I read that their intended use case for the NPU is background tasks when the GPU is already loaded but that seems like a very niche use case.

> Everything I see advertised for (blurring, speech to text, etc...) are all things that I never felt like my non-NPU machine struggled with.

I don’t know how good these neural engines are, but transistors are dead-cheap nowadays. That makes adding specialized hardware a valuable option, even if it doesn’t speed up things but ‘only’ decreases latency or power usage.

I think a lot of it is just power savings on those features, since the dedicated silicon can be a lot more energy efficient even if it's not much more powerful.

"WHAT IS MY PURPOSE?"

"You multiply matrices of INT8s."

"OH... MY... GOD"

NPUs really just accelerate low-precision matmuls. A lot of them are based on systolic arrays, which are like a configurable pipeline through which data is "pumped" rather than a general purpose CPU or GPU with random memory access. So they're a bit like the "synergistic" processors in the Cell, in the respect that they accelerate some operations really quickly, provided you feed them the right way with the CPU and even then they don't have the oomph that a good GPU will get you.

If you want a small adventure, you could see which HID device those keystrokes show up on, and they might be remappable courtesy of showing up on a HID device for that specific button. Failing that, they most likely come from either ACPI AML code or from the embedded controller (EC). If the former, it’s not that hard to patch the AML code, and maybe Copilot could do it for you (you use standard open source tooling to disassemble the AML blob, which the kernel will happily give you, and then you make a patched version and load it). If the latter, you could see if anyone has made progress toward finding a less silly way to configure the EC.

(The EC is a little microcontroller programmed by the OEM that does things like handling weird button presses.)

There are also reports of people having decent results using keyd to remap the synthetic keystrokes from the copilot button.

(The sheer number of times Microsoft has created totally different specs for how OEMs should implement different weird buttons is absurd.)

> There is also a chicken and egg problem of software being dependent on hardware, and hardware only being useful if there is software to take advantage of its features.

In the 90s, as a developer you couldn't depend on that a user's computer had a 3D accelerator (or 3D graphics) card. So 3D video games used multiple renderers (software rendering, hardware-accelerated rendering (sometimes with different backends like Glide, OpenGL, Direct3D)).

Couldn't you simply write some "killer application" for local AI that everybody "wants", but which might be slow (even using a highly optimized CPU or GPU backend) if you don't have an NPU. Since it is a "killer application", very many people will still want to run it, even if the experience is slow.

Then as a hardware vendor, you can make the big "show-off" how much better the experience is with an NPU (AI PC) - and people will immediately want one.

Exactly the same story as for 3D accelerators and 3D graphics card where Quake and Quake II were such killer applications.

They are still including the NPU though, they just realised that consumers aren't making laptop purchases based on having "AI" or being branded with Copilot.

The NPU will just become a mundane internal component that isn't marketed.

What seems to be delivered by NPUs at this point: filtering background noise from our microphone and blurring our camera using a watt or two less than before.

Yes, the idea before the whole shove LLMs into everything era was that small, dedicated models for different tasks would be integrated into both the OS and applications.

If you're using a recent phone with a camera, it's likely using ML models that may or may not be using AI accelerators/NPUs on the device itself. The small models are there, though.

Same thing with translation, subtitles, etc. All small local models doing specialized tasks well.

Not sure about all NPUs, but TPUs like Google's Coral accelerator are absolutely, massively more efficient per watt than a GPU, at least for things like image processing.

I have a Strix Halo 395 128GB laptop running Ubuntu from HP. I have not been able to do anything with the NPU. I was hoping it could be used for OpenCL, but does not seem so.

What examples do you have of making the NPU in this processor useful please?

The Apple M series chips are solid for inference.

Is that because of the actual processing unit or because they doubled the width of the memory bus?

Really, the best we can do with the NPU is a less battery intensive blurred background? R&D money well spent I guess...

> NPUs were pushed by Microsoft, who saw the writing on the wall: AI like chatgpt will dominate the user's experience, edge computing is a huge advantage in that regard

Then where is a demo application from Microsoft of a model that I can run locally where my user experience is so much better (faster?) if my computer has an NPU?

I think the reason why NPUs failed is that Microsoft's preferred standard ONNX and the runtime they developed is a dud. Exporting models to work on ONNX is a pain in the ass.

> AI like chatgpt will dominate the user's experience

I hope not. Sure they’re helpful, but I’d rather they sit idle behind the scenes, and then only get used when a specific need arises rather than something like a Holodeck audio interface

I'm not too familiar with the NPU, but this sounds a lot like GPU acceleration where a lot of the time you still end up having everything run on the CPU since it just works everywhere all the time rather than having to have both a CPU and an NPU version.

What software are they using for that, and how did they know ahead of time that the software would use their NPU?

> circuits will be relegated to some mothball footnote in the same manner as AVX512

AVX512 is widely used...

NPUs are a separate accelerator block, not in-CPU SIMD. The latter exists for matrix compute, but only in the latest version of AVX which has yet to reach consumer CPUs.