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This isn't ai slop. It's fascinating and well written. But I learned something really basic - i didn't know that the ANE (and the data pipeline around it) was

This isn't ai slop. It's fascinating and well written. But I learned something really basic - i didn't know that the ANE (and the data pipeline around it) was designed for CNN rather than transformers. It's always been an open loop in my head, wondering why the ANE was less impactful than i understood it should be.

msdz 4d ago
> 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…
- eastbound 4d ago
  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 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?") - and you tout it as "16 Neural Engine cores" during the sales, with no associated software, no listed material feature, just hand-waving, - Siri maxxes out at "There is no contact named 'What's the weather today' in your agenda", Then can't really claim that Apple engineers' problem was really the bad luck that ML wasn't the determining part of the future. It's more like misreading the room for 5 to 10 years straight. Apple engineering's excellence on vertical integration and supply chain control gave them absolute power over our world (with merit), it just failed at that particular project. Which occupies 40% of our CPUs.
  - adastra22 4d ago
    The ANE hasn't been sitting empty for 10 years. All those Photos features like face recognition and auto classification run on ANE.
  - kergonath 4d ago
    > 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?
  - alwillis 4d ago
    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.
    - sroussey 4d ago
      Dictation, text to speech, some of that computational photography, etc too!
- troupo 4d ago
  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.
  - kergonath 4d ago
    > 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.
    - troupo 4d ago
      > Silicon design does not have the same time scale than software. "Neural Engine" has been a part of iPhones since 2017. So, in development since at least 2013, possibly earlier. And it targeted the rather well established, known, and widely used ML practices. GPT-like models didn't become even remotely useful until at least 5 years later.
riedel 4d ago
A lot of neural engine, particularly in the embedded domain (ARM/RISC MCUs) have the same problem. Designing other models means on top of this means a lot of profiling to get convolution blocks right to get good speedups. (We optimized this in the past e.g. using Neural Architecture Search on super networks)
jasode 4d ago
>i didn't know that the ANE (and the data pipeline around it) was designed for CNN rather than transformers. Multiple stories have reported that ANE came from Apple's self-driving car project that got canceled. (Makes sense since CNN is used for vision-related machine learning and enables cars to analyze their surroundings.) They spent 10 years and ~10 billion on research & development on a product that never got released so Apple is probably happy they're able to salvage some of that ai technology and put it in iPhones and Macs.
- ACCount37 4d ago
  Tesla also has its own NPUs for self-driving - and Tesla uses transformers for sensor fusion. My guess would be that the main use case for an NPU in iPhone just used to be image processing/computational photography. Thus the CNN bent. Also makes sense with the timing - back when iPhone first got its NPU, CV was the killer app for ML.
- stefan_ 4d ago
  This is pure sunk cost fallacy. CNNs were from the deep learning ImageNet heydays, but now everything in that domain is equally done better by transformers. All you are doing is wasting area and saddling software with outdated hardware, and myopic PMs insisting on its use will create inferior products. Now that sounds a lot like the Apple AI efforts..
deleted 4d ago
[deleted]
dimatura 4d ago
I designed and deployed several custom CNN-based AI models to ipad hosts around 2018-2020. It was a finicky process - CoreML didn't give you much control (or even feedback) on what parts of your model would run on the CPU, GPU, or ANE - but you could usually tell right away when they didn't, just based on big latency differences. Performance to power (and size) ratio was pretty good and I was impressed at the time, despite being generally not really happy about working with the apple ecosystem. I would've preferred something Android (or Linux)-based but there wasn't really anything comparable out there back then. (SBCs like the Jetson would've worked, but we would've had to figure out how to add an interface for human operators which would've been a big lift compared to just using off the shelf hardware).

msdz 4d ago

> 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…

eastbound 4d ago
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 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?") - and you tout it as "16 Neural Engine cores" during the sales, with no associated software, no listed material feature, just hand-waving, - Siri maxxes out at "There is no contact named 'What's the weather today' in your agenda", Then can't really claim that Apple engineers' problem was really the bad luck that ML wasn't the determining part of the future. It's more like misreading the room for 5 to 10 years straight. Apple engineering's excellence on vertical integration and supply chain control gave them absolute power over our world (with merit), it just failed at that particular project. Which occupies 40% of our CPUs.
- adastra22 4d ago
  The ANE hasn't been sitting empty for 10 years. All those Photos features like face recognition and auto classification run on ANE.
- kergonath 4d ago
  > 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?
- alwillis 4d ago
  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.
  - sroussey 4d ago
    Dictation, text to speech, some of that computational photography, etc too!
troupo 4d ago
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.
- kergonath 4d ago
  > 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.
  - troupo 4d ago
    > Silicon design does not have the same time scale than software. "Neural Engine" has been a part of iPhones since 2017. So, in development since at least 2013, possibly earlier. And it targeted the rather well established, known, and widely used ML practices. GPT-like models didn't become even remotely useful until at least 5 years later.

riedel 4d ago

A lot of neural engine, particularly in the embedded domain (ARM/RISC MCUs) have the same problem. Designing other models means on top of this means a lot of profiling to get convolution blocks right to get good speedups. (We optimized this in the past e.g. using Neural Architecture Search on super networks)

jasode 4d ago

>i didn't know that the ANE (and the data pipeline around it) was designed for CNN rather than transformers. Multiple stories have reported that ANE came from Apple's self-driving car project that got canceled. (Makes sense since CNN is used for vision-related machine learning and enables cars to analyze their surroundings.) They spent 10 years and ~10 billion on research & development on a product that never got released so Apple is probably happy they're able to salvage some of that ai technology and put it in iPhones and Macs.

ACCount37 4d ago
Tesla also has its own NPUs for self-driving - and Tesla uses transformers for sensor fusion. My guess would be that the main use case for an NPU in iPhone just used to be image processing/computational photography. Thus the CNN bent. Also makes sense with the timing - back when iPhone first got its NPU, CV was the killer app for ML.
stefan_ 4d ago
This is pure sunk cost fallacy. CNNs were from the deep learning ImageNet heydays, but now everything in that domain is equally done better by transformers. All you are doing is wasting area and saddling software with outdated hardware, and myopic PMs insisting on its use will create inferior products. Now that sounds a lot like the Apple AI efforts..

deleted 4d ago

[deleted]

dimatura 4d ago

I designed and deployed several custom CNN-based AI models to ipad hosts around 2018-2020. It was a finicky process - CoreML didn't give you much control (or even feedback) on what parts of your model would run on the CPU, GPU, or ANE - but you could usually tell right away when they didn't, just based on big latency differences. Performance to power (and size) ratio was pretty good and I was impressed at the time, despite being generally not really happy about working with the apple ecosystem. I would've preferred something Android (or Linux)-based but there wasn't really anything comparable out there back then. (SBCs like the Jetson would've worked, but we would've had to figure out how to add an interface for human operators which would've been a big lift compared to just using off the shelf hardware).