cnn-benchmarks VS neural-engine

> ResNet > VGG: ResNet-50 is faster than VGG-16 and more accurate than VGG-19 (7.02 vs 9.0); ResNet-101 is about the same speed as VGG-19 but much more accurate than VGG-16 (6.21 vs 9.0).

https://github.com/jcjohnson/cnn-benchmarks#:~:text=ResNet%2....

> Then we'll see somewhat simpler, less general, and cheaper devices that do "AI" operations with as little excess hardware baggage as possible. Nice.

Apple has already been doing this for a few years now. The NPU is totally different from the GPU or CPU on the die itself[1]. Nvidia is likely working on this as well, but I think a device that's a gaming/entertainment/crypto bundle is probably a better business move.

[1] https://github.com/hollance/neural-engine/blob/master/docs/a...

~38 TOPS at fp16 is amazing, if the quoted number if fp16 (ANE is fp16 according to this [1] but that honestly seems like a bad choice when people are going smaller and smaller even at the higher level datacenter cards so not sure why apple would use it instead of fp8 natively)

[1]: https://github.com/hollance/neural-engine/blob/master/docs/1...

yep. they have a neural engine that is separate from the CPU and GPU that does really fast matmuls https://github.com/hollance/neural-engine. it's basically completely undocumented.

Looks like the ANE ("Apple Neural Engine") cores are powerful but not as flexible/programmable as the GPU cores. There is no sign that LLM inference is possible with them or ever will be unless Apple either opens up the closed ANE software framework for extensibility or they extend the ANE framework to support modern LLMs themselves. I would not hold my breath.

If Apple would wake up to what's happening with llama.cpp etc then I don't see such a big role for paying for remote access to big models via API

Currently a Macbook has a Neural Engine that is sitting idle 99% of the time and only suitable for running limited models (poorly documented, opaque rules about what ops can be accelerated, a black box compiler [1] and an apparent 3GB model size limit [2])

OTOH you can buy a Macbook with 64GB 'unified' memory and a Neural Engine today

If you squint a bit and look into the near future it's not so hard to imagine a future Mx chip with a more capable Neural Engine and yet more RAM, and able to run the largest GPT3 class models locally. (Ideally with better developer tools so other compilers can target the NE)

And then imagine it does that while leaving the CPU+GPU mostly free to run apps/games ... the whole experience of using a computer could change radically in that case.

I find it hard not to think this is coming within 5 years (although equally, I can imagine this is not on Apple's roadmap at all currently)

[1] https://github.com/hollance/neural-engine