8 ms·

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 upfr

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.

dharma1 10y ago
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
codecamper 10y ago
I don't see any mention of offering these chips for sale. You can rent them it seems via cloud offerings & that's it.
- semisight 10y ago
  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.
  - knorker 10y ago
    I would assume that the API to use these is tensorflow. So... just use Google's machine learning cloud thingy. The software can build the community, where the supercharging is only available when you run it on Google cloud. (although GPU performance isn't bad either, so you don't have to, thus community)
- babo 10y ago
  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.
  - retox 10y ago
    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.
    - omarforgotpwd 10y ago
      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.
      - retox 10y ago
        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.
    - CydeWeys 10y ago
      > 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.
nickysielicki 10y ago
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? [1]: http://spectrum.ieee.org/semiconductors/design/the-death-of-moores-law-will-spur-innovation http://spectrum.ieee.org/semiconductors/design/the-death-of-...
- honkhonkpants 10y ago
  Apple designs their own CPUs. I think they'd be able to field a massively parallel FMAC chip if they thought that was a good idea. Where Apple really looks weak is in datacenters, networking, and cloud services.
  - nickysielicki 10y ago
    What does the iPhone of 2021 look like? I get the feeling from today's announcements that Google sees the 2021 version of Google Now as the selling point for their 2021 Nexus line. I don't think Apple is preparing to compete on that.
    - habitue 10y ago
      I would say they're already not competing on the assistant side. Siri is considerably worse than Google Now, even though it came out first
    - Razengan 10y ago
      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.
deleted 10y ago
[deleted]
mtgx 10y ago
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.
- DSingularity 10y ago
  What are they doing with RISC-V?
  - PeCaN 10y ago
    They dumped a bunch of money into it, so presumably they're at least interested.
agumonkey 10y ago
I'm surprised by the perf claims. Nvidia isn't doing kids play. The graph implied they were untouchable in terms of perf...
- azinman2 10y ago
  Ndvidia has to be general purpose. This is not and thus can be better optimized.
  - fiatmoney 10y ago
    "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.
    - azinman2 10y ago
      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.
      - fiatmoney 10y ago
        "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.
        agumonkey 10y ago

dharma1 10y ago

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

codecamper 10y ago

I don't see any mention of offering these chips for sale. You can rent them it seems via cloud offerings & that's it.

semisight 10y ago
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.
- knorker 10y ago
  I would assume that the API to use these is tensorflow. So... just use Google's machine learning cloud thingy. The software can build the community, where the supercharging is only available when you run it on Google cloud. (although GPU performance isn't bad either, so you don't have to, thus community)
babo 10y ago
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.
- retox 10y ago
  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.
  - omarforgotpwd 10y ago
    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.
    - retox 10y ago
      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.
  - CydeWeys 10y ago
    > 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.

nickysielicki 10y ago

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? [1]: http://spectrum.ieee.org/semiconductors/design/the-death-of-moores-law-will-spur-innovation http://spectrum.ieee.org/semiconductors/design/the-death-of-...

honkhonkpants 10y ago
Apple designs their own CPUs. I think they'd be able to field a massively parallel FMAC chip if they thought that was a good idea. Where Apple really looks weak is in datacenters, networking, and cloud services.
- nickysielicki 10y ago
  What does the iPhone of 2021 look like? I get the feeling from today's announcements that Google sees the 2021 version of Google Now as the selling point for their 2021 Nexus line. I don't think Apple is preparing to compete on that.
  - habitue 10y ago
    I would say they're already not competing on the assistant side. Siri is considerably worse than Google Now, even though it came out first
  - Razengan 10y ago
    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.

deleted 10y ago

[deleted]

mtgx 10y ago

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.

DSingularity 10y ago
What are they doing with RISC-V?
- PeCaN 10y ago
  They dumped a bunch of money into it, so presumably they're at least interested.

agumonkey 10y ago

I'm surprised by the perf claims. Nvidia isn't doing kids play. The graph implied they were untouchable in terms of perf...

azinman2 10y ago
Ndvidia has to be general purpose. This is not and thus can be better optimized.
- fiatmoney 10y ago
  "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.
  - azinman2 10y ago
    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.
    - fiatmoney 10y ago
      "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.
      - agumonkey 10y ago