Amazing analysis
Same author even found a bug in it https://eiln.github.io/posts/ane-dma.html
Retrospectively Reverse-Engineering Apple's Neural Engine
21–30 of 37 posts
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#22It's worth remembering that Apple is releasing a new framework (Core AI) this fall that goes beyond the Pytorch and Tensorflow workloads that the decade old Core ML framework allowed.
> Core AI allows your app to use the latest model architectures and inference techniques across the CPU, GPU, and Neural Engine.
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#23A lot of folks are/were saying that Apple has missed the boat when it comes to AI, in some (important) aspects that is correct, but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017, before the AI hoopla really kicked off:
* https://en.wikipedia.org/wiki/Neural_Engine
* https://apple.fandom.com/wiki/Neural_Engine
"AI" has grown much more since then, and there have been important developments that Apple has not deployed (well), but I think they were looking ahead a little more than most at the time (even if events 'got away' from them subsequently).
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#24A lot of folks are/were saying that Apple has missed the boat when it comes to AI, in some (important) aspects that is correct, but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017, before the AI hoopla really kicked off: * https://en.wikipedia.org/wiki/Neural_Engine * https://apple.fandom.com/wiki/Neural_Engine "AI" has grown much more since then, and there have been import…
It may not matter that they're "sleeping on it". AI is going to disrupt all tech companies.
Clearly software and SaaS has no moat. I just saw someone vibe code a fully functional Photoshop in a week of Astra use. I'm thinking even big tech is not immune to this. Even the mighty infrastructure players.
Vibe hardware is just getting started, and I'm hoping we see competitors to iPhone and Android and MacBook. It's less expensive to build and test things now, which might lead to a Cambrian explosion of new hardware startups.
The world will greatly benefit from this.
It's funny that American tech giants invented AI, because AI is going to unseat American tech giants.
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#25Earlier quoted context omitted.
> But I learned something really basic Same for me! Also, just imagine being the group at Apple responsible for designing this section of the chip, starting probably almost a decade back – under the constant uncertainty of not knowing what direction ML workloads would develop in…
ML research was a rather known quantity, or the separate "Neural Engine" CPU explicitly aimed at existing ML pipelines wouldn't exist. However, very few used it for anything, even within Apple. I feel like it was a huge wasted opportunity.
> ML research was a rather known quantity, or the separate "Neural Engine" CPU explicitly aimed at existing ML pipelines wouldn't exist.
No, not really. Transformers were just one of the possible directions. Silicon design does not have the same time scale than software. Now, everyone is using transformers so it becomes harder to do anything else, and it’s been the case long enough that hardware had some time to align (but is still lagging). But who’s to say that a different architecture published last year won’t take the world by storm 2 years from now?
It’s easy to say it in hindsight, but transformers took a bit of effort to get where they are now.
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#26Earlier quoted context omitted.
> But I learned something really basic Same for me! Also, just imagine being the group at Apple responsible for designing this section of the chip, starting probably almost a decade back – under the constant uncertainty of not knowing what direction ML workloads would develop in…
I'm all for compassion, but engineers knew the NE was empty when it sat idle for 10 years on our computers. - when you're given no usecase for your engineering piece, apart from "detour characters in pictures". It's an exageration but AI's contributions in iOS aren't visible; Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collag…
> an exageration but AI's contributions in iOS aren't visible
If you want to ignore them, that’s right. In the real world, they’ve been talking about ML and how it’s making pictures or such-and-such aspect of the OS better for about a decade now. It might not be flashy, but it is used throughout the OS.
> Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collage feature working on the main CPU, and it has the same social effect as green bubbles in iMessage ("ah. Tourists on your photos. iPhone user?")
The feature to do this has been in the Photos application for years, what are you talking about?
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#27A lot of folks are/were saying that Apple has missed the boat when it comes to AI, in some (important) aspects that is correct, but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017, before the AI hoopla really kicked off: * https://en.wikipedia.org/wiki/Neural_Engine * https://apple.fandom.com/wiki/Neural_Engine "AI" has grown much more since then, and there have been import…
IMHO, I think Apple is poised extremely well. They didn't blow billions of dollars chasing models that are becoming commoditized. So many interesting models can now be run locally. All the big AI players have to pay even more to run those models when Apple will happily sell you the hardware, and you pay for the electricity. There will always be a place for some many-billion parameter model but as time progresses I think fast local models that keep data on site will always be a valuable, and Apple will happily sell you something to run them.
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#28Earlier quoted context omitted.
> But I learned something really basic Same for me! Also, just imagine being the group at Apple responsible for designing this section of the chip, starting probably almost a decade back – under the constant uncertainty of not knowing what direction ML workloads would develop in…
I'm all for compassion, but engineers knew the NE was empty when it sat idle for 10 years on our computers. - when you're given no usecase for your engineering piece, apart from "detour characters in pictures". It's an exageration but AI's contributions in iOS aren't visible; Meanwhile Google has features that people actually notice like removing tourists from your holidays photos — worse: it's mostly a simple collag…
Some features using the Neural Engine:
* Face ID since the iPhone X released in 2017
* fall and crash detection
* Live captions in videos, calls, and spoken audio
* facial recognition in the Photos app
* voice isolation in calls
There's more, but I'll stop there.
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#29A lot of folks are/were saying that Apple has missed the boat when it comes to AI, in some (important) aspects that is correct, but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017, before the AI hoopla really kicked off: * https://en.wikipedia.org/wiki/Neural_Engine * https://apple.fandom.com/wiki/Neural_Engine "AI" has grown much more since then, and there have been import…
> but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017
but missing the boat means you haven't succeeded not that you had some kind of initial foothold. haven't you seen the big short:
"i may have been early but i'm not wrong"
"it's the same thing"
Re: Retrospectively Reverse-Engineering Apple's Neural Engine
#30A lot of folks are/were saying that Apple has missed the boat when it comes to AI, in some (important) aspects that is correct, but I think it's worth remembering that Apple added the Neural Engine to A-series chips in 2017, before the AI hoopla really kicked off: * https://en.wikipedia.org/wiki/Neural_Engine * https://apple.fandom.com/wiki/Neural_Engine "AI" has grown much more since then, and there have been import…
Apple's Neural Engine is equivalent to the "NPU" hardware you would find on many cheap ARM SBCs. It's a dedicated coprocessor with a very low performance target, not intended for giant transformers or LLM acceleration. These NPUs are not particularly hard to design, and their critical flaw is that they don't scale very well. CUDA "won" because a bigger GPU meant having an utterly massive amount of CUDA cores to delegate ALU work to. NPUs/Neural Engine has the opposite problem, where spending $10,000 on an M1 Ultra only gets you ~2x better NPU performance versus the baseline $600 M1 chip. NPUs and Neural Engines are essentially dark silicon on the majority of devices with them, their uses are few and far between.
Considering Apple's refusal to sign Nvidia's ARM/CUDA drivers, it is pretty clear how Apple missed the boat here. Apple Silicon could have dominated the datacenter rollout if macOS supported CUDA properly. The Mac Pro would probably not have been cancelled if the PCI lanes could be used for normal datacenter GPUs and CUDA workloads. The excellent Thunderbolt bandwidth present on so many Macs is wasted supporting RDNA but not eGPU enclosures. There are several hardware features that Apple holds back for no good reason, handing Nvidia the lead in certain markets.
The only thing that stopped Apple from riding AI to the top was their own petty grudge towards Nvidia. Simple changes to macOS would have destroyed Nvidia's Grace CPU sales and made Apple Silicon the crown prince of the AI boom, at zero risk to themselves. The only thing Apple really needed was their own Mellanox equivalent.