This is huge. If they really do offer such a perf/watt advantage, they're serious trouble for NVIDIA. Google is one of only a handful of companies with the upfront cash to make a move like this.
I hope we can at least see some white papers soon about the architecture--I wonder how programmable it is.
There's no way Google lets this leave their datacenters. Chip fabrication is a race to the bottom at this point. [1]
Google is doubling down on hosting as a source of future revenue, and they're doing that by building an ecosystem around Tensorflow.
What I think is interesting is how weak Apple looks. Amazon has the talent and money to be able to compete with Google on this playing field. Microsoft is late, but they can, too.
Where's Apple? In the corner dreaming about mythical self-driving luxury cars?
Apple's strength is in consumer (and to a lesser extent, developer) ecosystems; the cozy comfortable bubble you get when you're surrounded by everything Apple. Getting access to your stuff across multiple devices is virtually effortless and continually seamless, with almost no configuration required.
Whether that's good or not may be arguable, but it's certainly a selling point for many and I don't see Google or any other company's offerings approaching the same experience, and I suspect that's by design; they have to be more open and support all devices but that kinda dilutes everything. Apple will only get stronger in that aspect IMO.
I'm not sure I agree with you, long term (about the chips). I think that the value here is in the ecosystem. If Google can compete with CUDA, they'll be doing really well.
> There's no way Google lets this leave their datacenters. Chip fabrication is a race to the bottom at this point. [1]
I’d hope someone somewhere steals the blueprints and posts all of them publicly online.
The whole point of patents was that companies would publish everything, but get 20 years of protection.
But by now, especially companies like Google don’t do so anymore – and everyone loses out.
EDIT: I’ll add the standard disclaimer: If you downvote, please comment why – so an actual discussion can appear, which usually is a lot more useful to everyone.
There's little need for anyone to steal the blueprints. It's unlikely there's anything particularly "special" there other than identifying operations in Tensorflow that take long enough and are carried out often enough and are simple enough to be worth turning into an ASIC. If there's a market for it, there will be other people designing chips for it too.
Same misuse happens with copyright. Both were invented to foster publishing and not creating life-long monopolies. The life times of copyright and patents must be way shorter as well. Everyone builds on something that came before. It's impossible to build a better bike if you have to test drive on a street with patent mines.
Re EDIT: Downvotes must be comment-mandatory or not allowed otherwise.
Sure, but that's the deal. I'll buy the latest nVidia 1080 card as soon as I can but renting these custom chips per minute would be a way better option for me.
GPUs also have this nice side effect of being great at playing games on. Purely as a guess I'd think that the gaming market is bigger than the AI researcher market.
In a future where AI is everywhere, Nvidia hopes it can sell GPUs by the hundreds and thousands to large data centers. You can make a lot more money a lot faster selling your hardware this way, and Nvidia is very interested in it judging from how much they talked about it at their recent conference.
I would be surprised if they weren't working on their own specialised chips then, though Google have the advantage of already having the software specs to build for.
> Purely as a guess I'd think that the gaming market is bigger than the AI researcher market.
Machine learning isn't just targeting the AI researcher market though -- it's widely used by a huge number of companies, and of course, by many of Google's most important products. I would argue that those markets combined are larger than gaming.
Yup, I assume they're gonna keep them in house as a competitive advantage for a time. I doubt they'll do it forever; the most valuable part of NVIDIA's CUDA is the ecosystem, and I think Google knows that.
Quantum computers, OpenPower, RISC-V, and now this - I'm really liking Google's recent focus on designing new types of chips and bringing some real competition into the chip market.
it's ASIC tuned for specific calculations, I'm sure it's better power consumption than general purpose GPUs. Same as crypto mining ASIC's crush GPU's in terms of power efficiency.
There isn't much data yet but I'm also guessing they probably have access to much more RAM than NVidia cards and can process much bigger data sets
"General purpose" isn't that general, if you look at the actual operations they support and their threading model. It's already fairly optimized for these sorts of operations, and this amount of claimed headroom makes me suspicious.
Google has a lot of potential options that NVidia doesn't have. They can size their cache heirarchy to the task at hand. They can partition their memory space. They can drop scatter/gather. They can gang ALUs into dataflows that they know are the majority of machine learning workloads. They can partition their register file at the ISA level or maybe even drop it entirely. They can drop the parts of the IEEE754 floating point spec they don't need and they can size their numbers to the precision they need.
The fact that I can compile arbitrary programs for the GPGPU means it is general purpose. NVIDIA isn't writing softmax or backprop into silicon as a CPU instruction.
Look at how much faster ASICs for bitcoin mining are than the GPU... orders of magnitude.
"Backprop" isn't even close to something that would be a "CPU instruction", it's an entire class of algorithm. It's like saying "calculus" should be a CPU instruction. Matrix multiplication & other operations, on the other hand, do neatly decompose into such instructions, which have been implemented by NVidia et al., since that's the core set of functionality they've been pushing for like a decade now.
Additional die space on additional functionality might hurt the power envelope (which is where the focus on performance / watt rather than performance kicks in) but it doesn't make your chips slower per se.
That was my impression too. ML under the hood was a lot of linear algebra, not very different than most shaders. But maybe Google decided to hardcode a few important ML primitives because the ROI was that good in terms of grabbing customers. Also they might have very large scale applications not found elsewhere that motivates this.
Ok I was obviously oversimplifying things but my point is since we can only speculate, it's clear that when you know specific algorithms/math operations/memory layouts/applications you want to optimize for you can create dedicated chips that optimize and do that quickly. That bitcoin miners are all dedicated chips and run circles around GPUs demonstrates exactly this fact.
Furthermore the fact that ML can be error tolerant means you also get to optimize certain floating point operations for speed or energy efficiency at the cost of accuracy. NVIDIA doesn't get to do this in their linear algebra support.
Bitcoin mining is an extremely well-defined task compared to machine learning. It remains to be seen how general these TPUs are in practice - whether they will support the neural network architectures common two years from now.
If they balance compute to memory better than GPUs, you could definitely see a 10x. GPUs have large off chip memory and small caches (like 256kb). Cost to going to off chip memory can be 1-2 orders of magnitude more than on chip memory. You can certainly fit 4+MB on modern processors, but they likely bought designs from a company like Samsung because designing high performance, low power memory cells is tricky. I'm surprised they were able to keep things a secret.
I hope we can at least see some white papers soon about the architecture--I wonder how programmable it is.