Apple Is Working on a Dedicated Chip to Power AI on Devices

Post by [deleted] » Sat, May 27, 2017, 2:58 AM UTC

wklauss wrote: post #55

jacquesm wrote: post #41

Earlier quoted context omitted.

I'd bet on this being an ASIC, doing this on an FPGA with any serious size matrix would require a very expensive FPGA, whereas an ASIC would allow more gates in a smaller volume and would consume less power to boot. 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.

> 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.

Post by bitmapbrother » Sat, May 27, 2017, 3:00 AM UTC

npgatech wrote: post #75

gigatexal wrote: post #70

Good. But Google beat them to it already.

How so? Google developed a server class TPU for Datacenters. Apple is trying to build on-device low powered custom chip.

Post by dnautics » Sat, May 27, 2017, 3:03 AM UTC

highd wrote: post #14

This is probably going to be a hyper parallel fixed point / integer engine like TPU gen1. Doing fast matrix multiply over really small fields is very subpar on CPUs and GPUs. That was the initial reasoning behind TPU gen1 - improving runtime performance. 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-reconf…

Post by dnautics » Sat, May 27, 2017, 3:08 AM UTC

Symmetry wrote: post #29

samfisher83 wrote: post #4

Why not use a gpu. A lot of AI stuff is linear algebra: Multiply accumulate etc.

Just as going from scalar to vector instructions provides a speedup so does going from vector to matrix instructions. If you've got big vectors than the amount of parallelism exposed for more hardware execution resources used on isn't too big but the reduction in register file read port usage is pretty significant. Also, inference is usually happy with int8s whereas graphics workloads are mostly float32s. So you can…

Post by LrnByTeach » Sat, May 27, 2017, 4:08 AM UTC

RandomBK wrote: post #56

wklauss wrote: post #55

Earlier quoted context omitted.

> 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.

Not if you put it that way. Apple can simply make new AI features exclusive to newer phones with updated versions of the chips. If they open the chip up to developers, this effect can spill out to the app store. That would then provide the impetus to push consumers to upgrade.