From the manufacturers point of view phones not being future proof is a feature, not a bug, that way you'll upgrade to that new shiny item which will keep the profits rolling in.
I don't think a better AI chip will be a convincing argument to change a phone one year later.
With each iteration of phone, they add a shiny new feature that is ONLY available on the newer model.
With Steve Jobs gone, Apple is more and more seen as the emperor without clothes[0] that it is.
[0] I.e. yet another rebrander of Chinese goods with marginal differences from other franchises
Aren't all the iPhone chips ASIC's with the main one custom by those hardware people they acquired? Seems to be default expectation for whatever they add next. They sure as hell have the money, too. :)
DSPs are not nearly as good at matrix math as a GPU and the phone already has one of those. DSPs are typically good at signal processing in a fairly limited domain, they would not be calling it an AI chip if it was just a DSP.
I foresee it as similar to their M series co-processors - the first one was pretty basic, and more sensors and jobs have been given to the newer ones each year.
I think a lot of people in this thread are making incorrect assumptions about FPGAs implementations of neural network applications.
(1) forward networks are constant multiplications, i.e. Fixed shift and add. FPGAs are very nearly optimal architecture for programmable constant-shift-and-add
(2) individual neurons in a network can be bitwidth optimized and huffman encoded for bit-serial execution, FPGAs are a very nearly optimal architure for variable bit-width operations in a bit serial architecture with programmable huffman decoders [edited: huffman encoding, not hamming]
(3) running a forward network requires multiple channels of parallel memory with separate but deterministic access patterns. Most fpga architurs are designed with onboard ram specifically to be used this way.
(4) fpga architectures can be designed inherently fault and defect tolerant, like gpus disabling cores, but with finer granularity. Especially if the compilation is done in the cloud, the particular defect / yield profile can be stored for placement optimization.
(5) anything optimized for ASIC design will be necessarily so close to an FPGA that it may as well benefit from the existing programmable logic ecosystem to be flexibly optimized for a particular trained network. You can't just tape out an asic for every trained network, but based my previous points, you most likely can optimize the logic for a specific forward network to run on an FPGA better than any asic designed to run arbitrary networks
In terms of AI there is little reason to run it on the phone unless it's heavily used or needs or be low latency. Consider if they add a 100$ of computing power to a phone that sits unused 99% of the time they can just build a server using those same 100$ worth of parts that can then serve 100 phones saving 90+$ per phone including upkeep etc.
PS: This is the same logic why Amazon Echo is so cheap, it simply does not need much hardware.
And based on that they figured privacy was a good thing to aim for. Play to their strengths and differentiate based on that.
Facebook has Cafe2Go. Apple is working on this (and already has bindings optimized to use the ARM vector unit for DNN evaluation).
Running on device, if it can be done with reasonable power, is a win for everyone. Better privacy, better latency, and more robust operation in the face of intermittent connectivity.
I.e. Bitcoin mining went GPU->FPGA->ASIC, each with more investment required to design but higher overall performance in Hash/W. But that workload is known exactly.
Maybe Apple aims on those ? it will certainly achieve a much higher WOW factor, which is key here.
One question is if it will architecturally be closer to a GPU or an FPGA. The field moves so fast that it might make sense to "future-proof" a bit with a live-reconfigurable FPGA.