Dual-Edge-TPU-Adapter VS neural-engine

M.2 Slot 4.0 x4 > This adapter that supports dual-bus E Key M.2 E Key dual Coral TPU

The Coral Dual TPU uses both the lines of the E-key standard, but everything else just uses one. So basically every E-key slot can only use half of it. the adapter that I’m waiting is handmade from a guy just for this module. THIS GUY

while currently it is not in stock, this project provides various adapters for the dual coral (including a pcie adapter: https://github.com/magic-blue-smoke/Dual-Edge-TPU-Adapter)

Get in on this: https://github.com/magic-blue-smoke/Dual-Edge-TPU-Adapter

Someone is trying to do this with these pcie-switch chips if you want to get on a waiting list for the adapter. Now I want to know if these pcie-switch chips can be chained...

Thanks so much for replying! Is the DDA working reliably? For some reason the Amazon links are not going through... I'm looking at picking up one of these too as my R530 doesn't support bifurcation: https://github.com/magic-blue-smoke/Dual-Edge-TPU-Adapter

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

My question too. This semi-answer on the page seems to contradict itself (source: https://github.com/hollance/neural-engine/blob/master/docs/p... ):

"> Can I program the ANE directly?

Unfortunately not. You can only use the Neural Engine through Core ML at the moment.

There currently is no public framework for programming the ANE. There are several private, undocumented frameworks but obviously we cannot use them as Apple rejects apps that use private frameworks.

(Perhaps in the future Apple will provide a public version of AppleNeuralEngine.framework.)"

The last part links to this bunch of headers:

https://github.com/nst/iOS-Runtime-Headers/tree/master/Priva...

So might it be more accurate to say you can program it directly, but won't end up with something that can be distributed on the app store?

Read between their buzzwords. Apple's Neural Engine does nothing for training. It's purely for inference and it still requires the developers to go through their API. If a model uses a type layer Apple doesn't support, it's back to the CPU/GPU.