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Comment by danuker | Hacker News Reader

All these "papers" are just large companies flexing their hardware resources.

"Join our vendor-locked implementation" is all I hear. "Trust us by investing into writing software for hardware you can never own".

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I feel the same way. But beyond these concerns, I must admit I also feel a sense of sadness due to the inevitable rise of application-specific hardware. When I hear about these super-fast specialised chips, all I can think about is whether I'd have enjoyed sitting in my bedroom as a teenager hacking on them. And the answer is probably no. They can do very specific things, but they don't have the kind of freedom of programming I enjoy.

Maybe I just need to look at them differently, or maybe my perception of their capabilities is just wrong. I do like GPUs, for example. While they are ornery and limited in many ways, they are still flexible enough that you can do enjoyable general-purpose programming on them.

A type-writer or a piano is an application specific piece of hardware, yet people can still do an amazing variety of things with them.

In the last few decades we got really lucky that general purpose CPUs kept getting faster and faster. So many of us could get away with ignoring application specific computer hardware.

Btw, as far as I can tell, TPUs are really just a variant of GPUs with an emphasis on lower precision?

Have a look also at the kind of special purpose hardware people make to mine crypto-currencies. I think the challenge for your teenage hacker is not so much to make them do interesting things, but to design them? (Assuming the tools and simulators become cheap enough?) FPGAs seem like they are cheap enough for a teenager?

> Btw, as far as I can tell, TPUs are really just a variant of GPUs with an emphasis on lower precision?

GPUs and TPUs have substantially different architectures, it’s not accurate to consider a TPU an a variant of a GPU. See https://dl.acm.org/doi/pdf/10.1145/3360307, particularly section “Contrasting GPU and TPU Architectures”

looking only at the matrix multiply units (and ignoring the hardware multithreading in GPUs that was deliberately not part of the TPU architecture):

TPUv3: 2 cores, 2 128x128 matrix multiply units per core

V100: 80 cores, 8 4x4 matrix multiply units per core

Thanks for the background information!

It's funny you say that GPUs are general purpose.

It's almost like a universal law that generic stuff is helpful only to some extent, after which you need specialization. That's why we do PhDs I suppose. And that's why you have cardiologists.

You can still enjoy hacking with a consumer FPGA card. For ML one major problem would be memory (for a FPGA that costs the same as a GPU), there's no free lunch. But there are applications where FPGAs of the same price work better.

The sooner specialized hardware comes out the better in my books. The sooner companies deliver it, the sooner patents will inevitably expire and we can get some open source implementations. The golden age of ASICs for everyone is a decade or two away yet. But the sooner that day comes, the better for those of us who want the ownership of it. I'd love to integrate an embedded GPU for an onboard AI into my hobby electronics projects with a low power overhead. We're just not quite there yet.

They do occassionally sell TPU-enabled systems. My backorder from Mouser is still sitting there for several months but some day I too may have a TPU to hack on.

that is a totally different product which is only called TPU because the team who makes it wants to sound cool

Could you elaborate? I guess the person you're replying to is talking about the Coral stuff such as the "USB Accelerator" et al, dubbed "Edge TPUs";

I too am after one of those for my home-security camera system but I've never really looked at what they _are_.

Yes, Coral (edge TPU) are just custom designed ASICs mainly for inference (not training). No shared silicon with Real TPUs that Google runs in the data centers. They're just drafting off the marketing.

Thanks, gives me something to read about!

Google TPUs are inspiring everyone. Dieter Fox confirmed at Stanford HAI, NVidia's investing big in cloud accelerators too ;)

http://nvdla.org/primer.html

Owning hardware is hugely overrated. Renting it is clearly superior.

Not at 10x the price, even including maintenance.

There's only so much of the Internet you can route through a small number of points, before it starts to fail.

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