alt Hacker NewsTbh it's been the same in Windows PCs since forever. Like MMX in the Pentium 1 days - was marketed as basically essential for anything "multimedia" but provided somewhat between no and minimal speedup (v little software was compiled for it).
It's quite similar with Apple's neural engine, which afiak is used very little for LLMs, even for coreML. I know I don't think I ever saw it being used in asitop. And I'm sure whatever was using it (facial recognition?) could have easily ran on GPU with no real efficiency loss.
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Apple's neural engine is used a lot by the non-LLM ML tasks all over the system like facial recognition in photos and the like. The point of it isn't to be some beefy AI co-processor but to be a low-power accelerator for background ML workloads.
The same workloads could use the GPU but it's more general purpose and thus uses more power for the same task. The same reason macOS uses hardware acceleration for video codecs and even JPEG, the work could be done on the CPU but cost more in terms of power. Using hardware acceleration helps with the 10+ hour lifetime on the battery.
Using VisionOCR stuff on MacOS spins my M4 ANE up from 0 to 1W according to poweranalyzer
The silicon is sitting idle in the case of most laptop NPUs. In my experience, embedded NPUs are very efficient, so there's theoretically real gains to be made if the cores were actually used.
I have to disagree with you about MMX. It's possible a lot of software didn't target it explicitly but on Windows MMX was very widely used as it was integrated into DirectX, ffmpeg, GDI, the initial MP3 libraries (l3codeca which was used by Winamp and other popular MP3 players) and the popular DIVX video codec.