Comparing Google’s TPUv2 against Nvidia’s V100 on ResNet-50
blog.riseml.com
blog.riseml.com
Would assume Google is able to do that because of the less power required.
I am actually more curious to get a paper on the new speech NN Google is using. Suppose to be 16k samples a second through a NN is hard to imagine how they did that and was able to roll it out as you would think the cost would be prohibitive.
You are ultimately competing with a much less compute heavy solution.
https://cloudplatform.googleblog.com/2018/03/introducing-Clo...
Suspect this was only possible because of the TPUs.
Can't think of anything else where controlling the entire stack including the silicon would be more important than AI applications.
You can’t buy a TPU, it’s a cloud only thing. They also show it’s not a huge difference in both perf and time to converge (albeit only one architecture)
I would say kudos to V100 and this benchmark that breaks the TPU hype.
Or maybe reading it wrong?
That is close to half has much to use Google is it not?
BTW, The TPUs are also about twice as fast also.
Sounds like Google is pretty far ahead of Nvidia. Which really just makes sense as Google does the entire stack and just going to have the data to optimize the silicon.
About half the cost is hype?
I want in the cloud and not have to deal with updating, etc. Would think most are the same for anything of any scale. Could not imagine any longer building up rigs and dealing with all the issues. Plus much harder to scale.
But we also have the V1 TPU paper and can see the TPUs are able to use less joules per inference compared to an older Nvidia architecture. Was not that close. Just makes sense Google V2 TPUs would do the same.
Hope Google does a V3 TPU and then will share a V2 TPU paper like they did on V1 of the TPUs.
What is far more impressive of the TPUs is
https://cloudplatform.googleblog.com/2018/03/introducing-Clo...
If really doing 16k a second through a NN and at a price you can offer generally now that is incredible. I want this paper even more so.
Yes I can say it is a lot cheaper. That is what this article is all about.
You can do about twice the images per dollar using the TPUs with GCP versus using Nvidia with AWS.
Or what am I missing?
BTW, Google has released to the general public. What are you talking about?
"Google’s AI chips are now open for public use"
https://venturebeat.com/2018/02/12/googles-ai-chips-are-now-...
It is a lot of work. But mainly that TPUv1 only did inference while TPUv2 does training+inference.
Here we can see the results where the Google TPU gets almost twice result per dollar over Nvidia. But then Google should be able to iterate more quickly.
Take the move from using CNN to using capsule networks. The idea for Capsules came from Hinton and Google is going to be there first to optimize in hardware. This is the benefit of playing in all layers of the stack.
Or the using NN for text to speech and offering at scale. Google just has inherent advantages over Nvidia and now we get to see a little more concrete results. But hope we get a lot more similar and see if the Google advantage holds up.
These costs also ignore transferring and storing massive data sets in the cloud. In general the cloud is a huge pain and I'd avoid it like the plague unless I was caught and really, really needed the scalability. But even then that only works if you have a scalable implementation of the algorithm you are working on.
TPUv2 is has 1.27x-1.86x the images/s/$.
And the other chart titled: Cost to reach 75.7% top-1 accuracy.
Where TPUv2 costs 62.5% the reserved GPU instance and 42.6% the unreserved GPU cost.
Key takeaway from the article:
> While the V100s perform similarly fast, the higher price and slower convergence of the implementation results in a considerably higher cost-to-solution.
Depends on how much you plan to use the hardware. If it's running near continuously, total cost of ownership is very important. Power costs can quickly dominate TCO.
Those cards are also irrelevant to the comparison as they can't be bought in large capacities for ML workloads. We're talking about Titan-V's and DGX-1's here.
> Pricing is kind of irrelevant since they can subsidize this to create that story.
You seemed to imply they == google. My point is that it could cut both ways.
No matter how fast a system is on the inside you have to get data in and out of it -- at the very least to memory. SRAM takes too much area and there is a limit DRAM bandwidth despite technologies such as eDRAM and HBM. Some tasks are compute intensive, but for general tasks, a processor that is 100x faster would need 100x faster memory to really be 100x faster.
Thus advances in real-life performance are likely to be more like a factor of 2.
For training I never pay full price in the AWS cloud, rather I run interruptable instances and pay a fraction of the list price. People I know who train in the Google cloud seem to get interrupted all the time even though they are paying full price.
Inference is another story. Once you have the trained model, you will usually need to run inference many many more times than you run training and this gets more so the bigger scale you are running at. That hits your unit costs and it is where you need to pinch every penny.
> While the V100s perform similarly fast, the higher price and slower convergence of the implementation results in a considerably higher cost-to-solution.
I would expect a dedicated accelerator to need at least a 5-10X advantage to outweigh all the other infrastructure and ecosystem costs.
GPUs are more useful for a wide variety of data-parallel tasks, and many more NN frameworks work on top of CUDA than work on the TPU.
In terms of horizontal scalability, nvidia has been rapidly iterating on increasing both memory and interlink bandwidth (including NVSwitch [1]), while each 'TPU' is actually 4 chips interconnected so likely has less upward scalability.
Also note that the tensor cores on a V100 take roughly 25-30% of the actual area. If Nvidia wanted to, they could probably easily make a pure tensor chip that beat the TPU in performance, could be produced in volume on their existing process, and also had full compatibility with their entire stack.
All in all, a 2x price/performance advantage for a hyper-specialized accelerator is basically a loss, just like how nobody installs a Soundblaster card anymore, how consumer desktops don't run discrete GPUs even though integrated graphics are a few times slower, or
[1] https://www.nextplatform.com/2018/04/04/inside-nvidias-nvswi...
Google has an advantage as they do the entire stack and can better optimize like we see here with half the cost.
I think they are actually farther along and more aggressive about exploring deep learning use cases in production than Google today; augmenting real data with extensive simulation is really a far-reaching idea that comes directly from their gaming experience.
> So money is not an issue. It is tiny in the scheme of things.
Money of course is always an issue long term; otherwise why doesn't Google Fiber just spend tens of billions of dollars to build out its nationwide network? Because it will see negative ROI even if they succeed.
The TPU has to eventually make a real return to Google, and it won't if nvidia can spend the same amount of money and build a faster product and sell it to all the other cloud players, which I believe they definitely can.
Put another way, the TPU has to be cheaper to Google than buying nvidia GPUs after factoring in its development costs, whereas nvidia gets to amortize those dev costs over all other cloud providers and all other GPU customers. Google isn't about to sell the TPU to other cloud providers; the entire idea is to use it to drive Google Cloud adoption.
The TPU is a fine chip, but if you just look at the big picture, there is every sign that nvidia could build the same or better product for less money because it has far more synergies across the hardware and chip design stack; e.g. the TPU only has PCIe connectors, while nvidia has already worked with IBM to get NVLink into supercomputers [2]. For some workloads the TPU will likely be bandwidth-starved communicating with the CPU and main memory.
[1] https://nvidianews.nvidia.com/news/nvidia-introduces-drive-c...
[2] https://www.ibm.com/us-en/marketplace/power-systems-ac922/de...
As far as I am aware Nvidia does not even run a cloud do they? Obviously never going to have the production NN that Google has.
Google now has well over 4k NN in production and not sure if Nvidia has any? Well over a billion a day are using the Google NN. That data allows Google to iterate in ways that Nvidia just never would be able to.
But this was all theory and why starting to see a little more concrete results like this where Google with their TPUs able to charge 1/2 the price of using Nvidia is value. Then we also have the paper from Google on the Gen 1.
I would guess Google is working on a gen 3. Nvidia is trying to catch a moving target but without the data. So they are behind, trying to catch up, but missing an arm.
A perfect example of this phenomenon is Capsule network pioneered by Hinton. They use dynamic routing which is potentially going to require different approach to memory access as the pattern would be different than CNN or RNN.
Today the problem is memory access and no longer instruction execution. Google nailed the low hanging fruit with the Gen 1 TPUs. They have 65536 very simple cores. Now you have to go after memory access.
Your post is all over the place so a bit hard to respond. Google Fiber was NOT about cost. It was about AT&T and other established players with some local governments making it difficult for Google to access what they needed to be able to compete.
I hate debating something with someone that is doing what you are doing. Google Fiber? Really?
"I think they are actually farther along and more aggressive about exploring deep learning"
I do a LOT of surfing on sites and can easily say this is the craziest thing I have read in a bit. You are honestly comparing Nvidia to Google? Really?
Google solved Go a decade early. Hinton did the Capsule networks and basically the farther of DL. Well made it actually work. What breakthrough came from Nvidia?
A single one?
There is so much crazy stuff in your posts this must be driven by something else and something emotional? Your points are just not based on reality. Is this really about Google firing Damore?
BTW, Nvidia read the Google Gen 1 TPU paper and why we see them doing similar things. But Google is going to move to addressing the memory access problems as that is the next area to improve. Once Google figures it out then you will see Nvidia just copy the approach like they are doing with the gen 1 TPUs.
I listened to this Nvidia presentation on YouTube and they were basically quoting the Google TPU paper. Talking about using 8 bit, integers, etc, for inference.
Google will release the gen 3 and then share a paper on the gen 2 and we will see Nvivida then try to copy that one. Nvidia always a couple of steps behind.
But I am a super curious person and can you share what this is really all about?
I'm not sure why you think I must be conspiratorial, although I will admit the thesis that 'Nvidia is an AI leader in software' is unusual, but ultimately I think well-supported by the public record and some diligent research.
I've been watching Nvidia for awhile, and one thing you notice quickly is that, much like Apple, they don't pre-announce or oversell vaporware; they tend to only announce things that they have already worked on for years and are imminently available.
> As far as I am aware Nvidia does not even run a cloud do they?
They don't run a public cloud yet, although they are making noises in that direction [1]. GPU Cloud right now is just a place where you get packaged Docker images (and then run them on AWS, GCE, what have you), but I don't think the branding is accidental—they are setting it up so if they decide to build a public cloud, ML researchers will already be familiar with the term.
They are also doing distributed cloud GPUs direct to consumer via Cloud Gaming [2].
Internally, they have gone the HPC/supercomputing route to develop their own ML stack, rather than Google/MS/AWS hyperscaler route [3]. They basically built their own supercomputer based on Voltas, and they use it internally to do everything from developing self-driving car software [4], including the simulation platform.
Note that AFAIK, the simulation platform is far ahead of other players in the field. We have heard time and again that 'data' is going to be the competitive advantage to Tesla (miles driven) and Waymo (mapping data). What if you can partially sidestep the issue by leveraging the ability of humans to actually define dangerous scenarios and rigorously test them outside of the constraints of road driving?
The platform literally has literally built the idea of 'regression testing' and translated it into the ML space and they are planning to deploy this into production systems in the next 1-2 years. From what I've heard from ML researchers, the end-to-end testing and deployment of NNs is still rather in its infancy, in terms of being able to change your network and then do mass inferencing on prior 'test cases' that you think are important.
> Google Fiber was NOT about cost. It was about AT&T and other established players with some local governments making it difficult for Google to access what they needed to be able to compete.
You are defining 'cost' far too narrowly, or rather not seeing how non-economic costs eventually translate into economic ones. The established players made it difficult for Google. This eventually translated into 1) higher legal fees to fight them 2) slower deployment rates and 3) higher operational costs for expansion. All these things obviously cost lots of time money and sharply lower the overall ROI of a project, hence why Google has essentially given up. There's only risk, no reward.
The point is not to compare the TPU project directly to Fiber (the two projects are very different), but just to address your point that 'cost doesn't matter to Google because they have a lot of money'. Companies that truly don't care about cost will very soon end up with very little money. Put another way, I don't think the eventual reward from continuing TPU development will be more profitable than simply buying GPUs from Nvidia down the line.
> Now you have to go after memory access.
Nvidia might be better-positioned to optimize memory access than Google is, they have their own fabric and work with a large variety of partners to optimize their ML/DL workloads.
> Your post is all over the place so a bit hard to respond.
Well, the crux of my argument is that:
1. Chip development is an expensive business
2. Nvidia is good at building chips; the Volta is already within striking distance of the TPU using only ~25% of its die area for tensor units. As NNs grow, inter-node scalability will become more important, and Nvidia has large advantages in interconnect that will show up in large-scale deployments (like supercomputers, where I expect a lot of DL to happen)
3. Google's business strategy only allows it to spread development costs over its own deployment, while Nvidia lets many other players pay for the dev cost, including competing hyperscalers, HPC, gamers, and carmakers. Nvidia's potential 'ecosystem' is much larger than Google's. Historically, we've seen that structural advantage be very hard to surmount.
1-3 means that in the long run, a 'go-it-alone' strategy like Google's is unlikely to win a protracted R&D fight.
> Google solved Go a decade early. Hinton did the Capsule networks and basically the farther of DL. Well made it actually work. What breakthrough came from Nvidia?
Yes, Deepmind has made some great strides, but how does that directly fund TPU development and give it a competitive advantage? The fact that those papers are published means that any talented researcher at Nvidia can replicate the work, then run and optimize it on their GPU architecture.
> There is so much crazy stuff in your posts this must be driven by something else and something emotional? Your points are just not based on reality. Is this really about Google firing Damore?
I'm not sure why you are so convinced that only a crazy person with a beef about Google can have a differing opinion from you. Do you work on the TPU team or something?
> Google will release the gen 3 and then share a paper on the gen 2 and we will see Nvivida then try to copy that one. Nvidia always a couple of steps behind.
Where's your evidence that Nvidia is simply copying Google, rather than both engineering teams viewing the same problems and converging to similar solutions?
Note that even if it is true that Nvidia is simply 'copying Google', they have the resources to beat Google it its own game, by leveraging process, memory, CUDA, etc. You've studiously avoided addressing this point.
[1] https://www.nvidia.com/en-us/gpu-cloud/deep-learning-contain...
[2] http://www.nvidia.com/object/cloud-gaming.html
[3] https://www.nextplatform.com/2017/11/30/inside-nvidias-next-...
"You are defining 'cost' far too narrowly "
Agree but this is opposite here as Google has the advantageous compared to Nvidia. So something does not add up? I mean what you pointed out is a disadvantage for Nvidia? I mean heck the canonical AI framework Google controls. 100k stars on Github is just incredible and can only think of K8s doing anything similar?
A big difference I think you are missing is Google did NOT run their operation on Google Fiber. But they do on the TPUs. Google has over 4k production NN and the amount of money they save running them at less than 1/2 the cost for their own stuff versus using Nvivida is a huge amount of money.
But also keeps growing. Google has a fundemental advantage over their competitors having the TPUs. A perfect example is their new text to speech.
Speech using a NN at 16k samples a second at a reasonable price would be impossible without the TPUs.
https://cloudplatform.googleblog.com/2018/03/introducing-Clo...
Does NOT appear Volta is in striking distance. But more importantly Google will do a gen 3 and 4, etc. They have the data to iterate and Nvidia just does not.
But more importantly Google does the entire stack and Nvidia does NOT. AI it is so important to do the entire stack for efficiency reasons. Plus Google controls the canonical AI framework with TF.
"Google's business strategy only allows it to spread development costs over its own deployment "
Well that clearly is untrue. Just take their text to speech sold as a service and the cost of doing on Nvidia would have been prohibitive. They could not even offer the service without the TPUs.
"Do you work on the TPU team or something? "
No. But I have been running into all this hate for Google with the alt right all around firing Damore that logic is lost.
I was talking to a Russian this morning on Reddit and he was delusional because of his hate for Google based on him thinking they are left wing extremest.
"they have the resources to beat Google it its own game "
This is the exact problem for Nvidia. They do not have the resources to compete. That is the exact problem.
Chips will come from the big players and NOT third parties in the future.
The entire dynamics of the industry have changed and actually a lot more like the past ironically.
Google, Amazon, FB and other big players will do their own silicon. Even Tesla is suppose to do the same.
The reason is because the people that buy the chips now run the chips which was NOT true in the past. Use to be a Dell purchased from Intel and sold the machine to someone.
The big difference today is the users of the systems are centralized with the big cloud providers. So they now get the data to improve the chips which just was not true in the past.
Plus it is looked at as being a competitive advantage.
So Apple does their own. Google does their own including the PVC on the device. Amazon and FB will also do their own.
Google did the same thing years ago with networking. They quietly hired the Lanai team to build all their own network silicon which significantly lower their cost.
Heck Google then created their own network stack to make it determinate. It is how it was possible to create Spanner.
Tech companies are so much bigger today they have the resources to do all their own stuff and own every layer of the stack instead of using third parties.
Google could never be what they are today if they had not built their own stuff. Could you imagine the cost of using SAN instead of them creating GFS?
Yes, Google has a large ML deployment, but so does Nvidia, which is not (currently) focused on direct-to-consumer public APIs, but actually doing deep learning and simulation at scale.
The hyperscaler approach to ML is not the only possible way to scale up, Nvidia chose to go the HPC/supercomputing route and basically built their own supercomputer from the ground up.
Both approaches have their advantages and drawbacks, but one thing that supercomputing approaches have is a focus on vertical scalability. It's not just about samples/second, but how big can you feasibly make and train an NN? Note that the national research labs are getting into the act, and those supercomputers are basically built in close collaboration with Nvidia [1].
I would really recommend spending some time on their website and watching some of their videos, e.g. [2]. Jensen Huang is completely bought into deep learning and NN and has re-oriented its company towards making sure Nvidia can dominate the space.
> They have the data to iterate and Nvidia just does not
This is where I fundamentally disagree with you. This was true 3 years ago, but not today, mostly because Nvidia is the default option for ML researchers right now and they are slowly but steadily enticing everyone to collaborate with them (not to mention their self-driving efforts, which generate troves of data directly).
> Just take their text to speech sold as a service and the cost of doing on Nvidia would have been prohibitive.
That's on their own deployment.
Google is #3 in the cloud space right now. It's Nvidia-powered AWS + Azure ML deployments competing against Google, which also deploys V100s as well as TPUs.
Although it's possible for a single vertically integrated player to beat the rest of the market (e.g. Apple) for a long period of time, it's a difficult, risky proposition and it usually helps if they started out with a huge advantage, which Google doesn't seem to have since they're starting at #3 in the cloud space.
> hey do not have the resources to compete. That is the exact problem.
I think, perhaps, you are still imaging the company as it was in 2012 or 2015, but the company's revenues and profits have grown substantially in the past years.
Nvidia's market cap is $132bn and they have a profit run rate of about $4bn - 5bn / yr.
Their R&D spend has averaged about $2bn / yr for the last 5 years or so; in fact they beat AMD/ATI into the ground while spending less on R&D. They can basically triple the amount of money their pour into research if they wanted to.
By comparison, Google spends about $15bn/yr on R&D, but that's split across far more projects.
> Google does the entire stack and Nvidia does NOT.
I'm going to have to strongly disagree with you on that one.
Google owns more of the deep learning end-to-end cloud stack, but they do not own more of the hardware, software, or firmware stack for accelerated computing.
Which 'ecosystem', the easier access to data (which Google does have) vs. controlling the hardware + frameworks + partnerships, is an open question. I tend to believe the latter, because Nvidia has many options to get its hands on data (they can partner with the other cloud providers), while Google would have to invest quite a bit to compete on Nvidia's terms.
The easiest example, which I keep coming back to and which you haven't addressed, is how is Google going to compete on memory fabric and node architecture? Nvidia is out there building NVLink, NVSwitch, and basically their own supercomputing nodes (DGX-2).
They are working ORNL to build some of the largest Volta deployments in the world, so they are rapidly building experience on doing deep learning at large scale as well. How would Google be able to match this if NN/DL development turns out to scale vertically (and we are seeing this in rapid YoY growth of layer depth and network size in DL).
Again, TF is not really a direct advantage for Google because it runs equally well on Nvidia hardware. If Google is so confident in the TPU winning out, why are they busy deploying Voltas in GCE?
If you want to do deep learning today, Nvidia is the go-to option because every deep learning framework is on CUDA, including cuDNN. If I want to use the TPU, I am stuck using GCE + Tensorflow (although Keras / PyTorch may soon have support), but with Nvidia I have the choice between every single cloud provider or my own local deployment, which is always ultimately cheaper than paying for cloud time. Google seems unlikely to sell you a TPU for your own DL box.
> Google, Amazon, FB and other big players will do their own silicon
It's certainly an interesting space now. MSFT is busy buying FPGAs from Xilinx and Intel/Altera as part of their strategy. Ultimately though, you seem to think that Nvidia is still a niche GPU maker from 2013 or so; it's not, it is larger than Tesla and certainly has more than enough funding, plus a very focused execution team and CEO.
> Google could never be what they are today if they had not built their own stuff. Could you imagine the cost of using SAN instead of them creating GFS?
I agree that the hyperscalers found significant savings by looking up the stack, but that has limits. They aren't building their own CPUs, for example. Chipmaking is a very, very expensive game.
[1] https://insidehpc.com/2018/01/using-titan-supercomputer-acce...
Not even sure where they are hosting them or even what they do? How about some color as you have me curious?
Have watched videos of Jensen but also watched an excellent almost 2 hour presentation from one of their VPs. He said a lot of things that were in the Google TPU paper which I found a bit funny. How you can use 8 bits and integers for inference for example. Said to me these guys are trying to catch up.
The problem is Amazon has that data NOT Nvidia. It is not in Amazon best interest to help Nvidia this is my exact point. The entire dynamics of the chip business have changed. You will see Amazon do their own just like Google has.
Once Google did the gen 1 TPUs they set the direction of you just can NOT buy off the shelve and compete long term.
The silicon is strategic for AI.
MS went the wrong direction in using a FPGA solution in addition to using Nvidia. But once again no data for Nvidia.
Market cap does not give you the money. But Google in 2018 will spend about 2x Nvidia 2017 sales! Yes you read that correct. Google on R&D will spend 2x Nvidia 2017 sales!
Google profits will be over 4x Nvidia total 2017 sales.
Once again Nvidia does NOT do the entire stack. I am not aware of any algorithm breakthroughs that came from Nvidia. I can not even name one AI expert Nvidia.
But the score board is papers excepted at NIPS. Nvidia did NOT get a single paper accepted that I saw at the conference?
Versus Google had more than anyone. 9% of all the paper accepted came from Google.
https://medium.com/machine-learning-in-practice/nips-accepte...
If Nvidia is playing in the entire stack how could they NOT get a single paper accepted at NIPS?
Or did I miss it?
If we look at Self Driving cars one of the most important AI applications Nvidia does not even show up on patents? Once again Google ahead by a mile.
https://www.theatlas.com/charts/r1iEkmKkz
Something in your post does NOT add up? Why if Nvidia is a player in the stack besides the silicon why do they NOT show up any where?
Google deploys both, TPUs and Nvidia, for a number of reasons I suspect.
The biggest is they want TF to be the canonical framework for AI and they MUST show not favoring their own solution until it is a done deal which is getting close.
In the TF will never run as well on Nvidia as they will on the TPUs. We can see hit here with about 1/2 the cost using the TPUs over Nvidia.
It is like saying Android would run as well as iOS on the Apple processors. It is all about controlling the entire stack like Apple has done and Nvidia is just not in a position to be able to.
Makes no sense to buy the processors so would not make any sense for Google to sell them to others. Not going to ever see that happen.
But I do think it is possible Google will sell the PVCs.
The ultimate problem is Nvidia is in perceptual catching up. Right now the big new thing that came from Hinton is Capsule networks and using dynamic routing. Google will have that optimized in silicon long before Nvidia will.
I suspect it will create the need for a different approach how you access memory in chip architecture.
But Capsule networks are heavy computationally and so silicon will matter a lot. Google has the algorithms and how they want to use in production at scale and then the money to execute in supporting in silicon. They just move way too fast for Nvidia to ever be able to catch up.
> How you can use 8 bits and integers for inference for example. Said to me these guys are trying to catch up.
I think it's interesting that you presume that only Google came up with the idea first, rather than 'reducing precision' to be a rather obvious idea that any chip designer or ML practitioner would have brought up. Again, can you please justify that?
I think where we're at a disconnect is that you equate AI leadership with publishing and patents, while looking at Nvidia, they are an extremely secretive organization that would probably avoid publishing what they see as a competitive advantage. This is similar to how Apple operates.
I used to work at finance, and the culture was the same way—banks had state-of-the-art models internally but would never share it. Published papers in academia were probably ~5 years behind what the banks had.
I do believe that Google (mostly Deepmind) is the leader in the research field, but note that they had to go out and buy that expertise.
> Google on R&D will spend 2x Nvidia 2017 sales!
Yes, but it's not all going into AI for sure, and definitely not into bankrolling the TPU effort. We should compare apples to apples here, surely?
> The entire dynamics of the chip business have changed. You will see Amazon do their own just like Google has.
So what about Nvidia's self-driving efforts? I've talked about it for about 3-4 posts now, with references to presentations and videos, and heard more or less crickets from you about it. I don't see how you can repeatedly say that Nvidia has no access to data when they clearly have a working product (Drive PX2) already, plus more (Drive Xavier) ready to be deployed in cars within the next ~18 months.
> Google deploys both, TPUs and Nvidia, for a number of reasons I suspect.
> The biggest is they want TF to be the canonical framework for AI and they MUST show not favoring their own solution until it is a done deal which is getting close.
Yes, but for those exact same reasons, the TPU will not be a strategic edge for Google and lower the ROI of working on the project.
You can't have it both ways: either the TPU is the secret sauce that drives Google Cloud adoption and gives them a big leg up in AI (in which case, they would want to leverage TF and make it 'run better' on the TPU than on other hardware), or else TF is a neutral platform and it doesn't benefit either party (which I actually agree with).
> It is like saying Android would run as well as iOS on the Apple processors. It is all about controlling the entire stack like Apple has done and Nvidia is just not in a position to be able to.
I think the analogy here is really apt, but also shows why I don't believe in Google's success here long-term.
The iPhone basically invented the smartphone market; its product was 10x better than any other competitor when it was introduced, and it was probably the majority of volume (and definitely profit) for years before Android was able to compete.
The TPU is not heads and shoulders above the competition. The Volta came out literally ~1 year after Pascal and had 10X the tensor throughput; you say that Google isn't standing still, but certainly neither will Nvidia.
Basically, Google is not starting from a 'commanding lead' position like Apple did. And we see today that even though Apple still leads in profits, Samusng is very close, and, Android is the vast majority of the market.
Larger ecosystems tend to beat fully vertical stacks in the long term. We see this across many markets and products. So why do you think this will be the exception?
But honestly Nvidia is so far behind in SDC and without any patents it is hard to see them competing.
https://www.theatlas.com/charts/r1iEkmKkz
Yes obvious would agree. But Google implemented late 2014 and Nvidia did NOT in 2014 or 2015 or 2016 as far as I am aware?
AI is not a secretive area. So if Nvidia had something we would know. On the lack of patents puts them in a very weak position. Especially with SDC.
" but note that they had to go out and buy that expertise."
This is one of the more stupid things I have read in a bit on the Internet.
In late 90s Larry Page was asked about using AI to make search better. He shared we are doing search to make AI better.
TPUs did NOT even come from Deepmind. But honestly knowing what to buy is important. But Google is miles ahead of everyone without Deepmind. TF also did NOT come from Deepmind. So many other things. I would actually say the Brain team has done a lot more in actual production than even DeepMind. But DeepMind is Google and rather dumb comment, no offense. How old are you?
Yes the TPUs are very strategic. It is how they were able to do AlphaZero. Or more importantly their new Speech offering at a reasonable cost. Without the TPUs that would not be possible and that is a strategic advantage for Google and why Amazon and everyone else will copy.
Buying off the shelve can NEVER give you a strategic advantage.
Google does NOT run their inference at scale on Nvidia. But also training has been moving to TPUs quickly for Google. They offer a choice but with the TPUs half the price as it shows how much better they are.
You want to get TF to be the canonical solution and then use your fundamental advantages. Just business 101.
"The TPU is not heads and shoulders above the competition. "
We can see the TPUs are heads and shoulders better. Heck they are half the cost. Much bigger advantage than the iPhone. But more importantly they will improve far faster than anything from Nvidia.
"Basically, Google is not starting from a 'commanding lead' position like Apple did. "
Google lead in AI is much, much larger than any of Apple. Heck Apple market share is about 14% and Google with Android has over 80% market share.
"Larger ecosystems tend to beat fully vertical stacks in the long term. "
There is no Nvidia eccosystem that I am aware of? The AI eccosystem is built around TF.
> But DeepMind is Google and rather dumb comment, no offense. How old are you?
Why are you getting all worked up? That's not an insult to Google, simply pointing out that their own organically grown corporate org (including Brain) was not adequate to do the cutting-edge research they felt they needed.
> AI is not a secretive area. So if Nvidia had something we would know. On the lack of patents puts them in a very weak position. Especially with SDC.
I disagree. Think about this the other way; if some company was quietly plugging away with large AI advances and deciding not to publish them, how would you even know? My evaluation of Nvidia's technology is based on their public presentations and the products that have already been released—products that every single AI practitioner on the planet buy and use, plus the 150+ car ecosystem partners that have decided to go with Nvidia's driving platform [1].
People who are far more deeply enmeshed in this technology than you or I have voted with their feet and decided to build their core competency for the next 5+ years on Nvidia's platform, while Waymo has maybe 2-3 major automotive partners?
> Google lead in AI is much, much larger than any of Apple. Heck Apple market share is about 14% and Google with Android has over 80% market share.
Bottom line, I definitely agree that Google is an AI leader, but I do not believe that the AI future will be run on TPUs, for the simple reason that chipmaking is a risky, expensive endeavour, and Nvidia has much more expertise than Google does in that regard, while having access to a larger partnership, ecosystem, and its own set of data and engineering.
Put it this way, the actual chipmaking stack is more important than the data stack when it comes to making chips. Just think about it—let's say you've run your thousands of NNs to benchmark the workloads on the TPU, and it turns out that CPU-TPU and TPU-TPU bandwidth is the real bottleneck. What do you do as Google? They have no expertise in building interconnects and scaling them, while Nvidia does.
Data only gets you so far, you still need to be able to do the semiconductor engineering + create partnerships, and in that regard Nvidia is light-years ahead.
To belabor the point, if the goal is to make chips, then being good at chipmaking is very important, and Nvidia is closer to Google in data, than Google is to Nvidia in chipmaking.
I will bet you that 2 years down the line, Nvidia will have abandoned its own TPU project and all major players just buying Nvidia chips, both for inferencing and training.
This is exactly the role Intel plays today in CPUs, and it's both natural and reasonable, and the largest reason is because of structural market factors, which you have never even responded to.
Google's cloud is a fraction of the size of AWS and Azure's — that means Nvidia makes far more money from Voltas than Google will ever save on the TPUs, and plough that right back into additional R&D. Business people demand a positive ROI. Where will the positive ROI from a TPU come from?
Google is and will be an AI leader. But it will certainly not be doing its own chips.
[1] https://www.nvidia.com/en-us/self-driving-cars/partners/
Deepmind is Google but only one aspect. Much of the best research does not even come from the Deepmind unit.
Nvidia had zero papers at NIPS accepted. Plus GANs, Capsule networks, AlphaGo and so many other breakthroughs come from Google.
But maybe I am just unaware. Can you provide some breakthroughs from Nvidia? Maybe I am just unaware?
SDC will be winner take all and Waymo is literally miles ahead of everyone else.
We have recent benchmarks done on Nvidia versus the TPUs and the TPUs are about 1/2 the price of using Nvidia for the same amount of work. That is a big advantage for Google.
But also that was gen 2 and suspect we will see a gen 3 soon which will be another step forward. Nvidia will constantly be trying to catch up.
"I will bet you that 2 years down the line, Nvidia will have abandoned its own TPU project and all major players just buying Nvidia chips "
This does not make sense to me and think you had a typo?
BTW, Unless Nvidia makes major advancement Google just could never use Nvidia for their own stuff. The cost would just be way too high. Perfect example is the new Google text to speech using a NN at 16k samples a second. There is just no way Google could have used Nvidia and offer at a competitive price. The joules per inference is just way too expensive with Nvidia.
Google would have loved to buy chips for their stuff from Nvidia. Problem is they just do not have anything they could use at a price they could offer at scale.
So unless Nvidia catches up you will not see Google use Nvidia for their services.
The big new advancement I suspect we will see with Gen 3 is different memory architecture to better support dynamic routing with Capsule networks which came from Hinton.
Then it will be a couple of years before we see the same from Nvidia.
BTW, what is different is nobody is going to be tied to any chip architecture like we had with Intel. Those days are gone. The common layer will be TF. Now has over 98k stars on GitHub.
Besides K8s what else got to 100k stars faster.
You must be joking. I've only been pointing you repeatedly to Nvidia breakthroughs, with references and links provided, for the last 5 posts. Either you are blind, or willfully ignorant.
More self-driving accidents are only going to accelerate the pace. 3 years from now the government will be requiring auto makers to use the Drive Constellation [1] for safety testing.
Google has nothing remotely comparable, neither in published research nor in announced products.
We've gone through something like 8 replies and thousands of words, and you have written exactly one sentence addressing Nvidia's developments.
> The joules per inference is just way too expensive with Nvidia.
The Drive Xavier is basically a giant inferencing engine on a power-constrained platform [2]. It will be shipping in quantity in 2019. There is no equivalent Google product even announced.
> Now has over 98k stars on GitHub.
The fact that you are resorting to GitHub stars to make your argument is utterly laughable.
CUDA and cuDNN is the real enabler, which every single DL framework today (including TF) supports. People trust Nvidia far more than they trust Google to be an ecosystem partner.
> Unless Nvidia makes major advancement
Looked through your social media posts, half of them are pro-Google fanboism.
Nvidia's makes major advancements every 6 months across the entire deep learning stack. I keep pointing you towards what they're doing, hoping you have something interesting to say, but all you have to offer is the same tired Google cheerleading.
Look, I'm a long-time investor in both companies and like them both very much. But it's quite obvious you have zero interest in doing even a smidgen of research about Nvidia nor their technology.
Anyway, thanks for the replies, but I'm no longer interested in continuing this convo. You don't seem to know anything relevant at all about Nvidia, nor are you interested in learning more, despite all attempt to point you towards interesting things that they're doing, and why their approach is unique.
[1] https://www.youtube.com/watch?v=lVlqggTiTzY
[2] https://www.engadget.com/2018/01/07/nvidia-xavier-soc-self-d...
I doubt we will see deaths with Waymo any time soon. But yes with Tesla and the others. Google has 20k cars on order for 2020. Google does it without a safety driver and no one else close. Nvidia even gave up with the real road and now copying Google in creating a virtual world.
Does not seem like you even understand what TF is?
Ironically investor in Nvidia and Google. Google since their IPO. Well not in the IPO but open market at the time of the IPO. Google will do better than Nvidia long term but there will be scraps for Nvidia so wanted a position.
Plus you will get create price action with Nvidia because of hype but have to watch it and know when to get out. Think safe for a couple of years.
Problem is Nvidia does not do the entire stack. AI you MUST do the entire stack.
But the other problem is Google has a vision of creating the singularity. I do not think Nvidia has a similar vision.
You can definitely do this on a GPU. We use the older auto-regressive WaveNets (not Parallel Wavenet) for inference on GPUs, with the newly released nv-wavenet code. Here's a link to a blog post about it:
https://devblogs.nvidia.com/nv-wavenet-gpu-speech-synthesis
That code will generate audio samples at 48khz, or if you're worried about throughput, it'll do a batch of 320 parallel utterances at 16khz.
Happy to answer questions!
Note also, that the ~2% performance difference is only on one model (ResNet-50) and cannot be generalized to all workloads/all of deep learning (at least not without further proof).
That is not all that close is it?
the TPU implementation applies very compute-intensive image pre-processing steps and actually sacrifices raw throughput
Thanks
In terms of how much compute power the TPU pre-processing needs I only have very rough numbers: I ran the same pre-processing while training ResNet-50 on a node with 4 GPUs and it was consistently utilizing >22 CPU cores (including all of the other CPU-tasks while training).
EDIT: you don't get sustained use discounts, either, at the moment. You can get either for GCP GPUs, though. Perhaps that will change once TPUs are out of beta?
Any idea of how much variation in accuracy you get on different training runs of the same model on the same hardware? My understanding is that model quality can and does vary from one run to the next on these kinds of large datasets - from a single observation, it's hard to know if the difference is real or noise.
Tracking down bugs in convergence is really costly in these settings. We had a problem in pre-processing that took us quite a while to figure out...
But what's going on when some of the implementations of a standard algorithm don't converge, and different hardware has different accuracy rates on the same algorithm? Are DNNs really that flaky? And does it really make sense to be doing performance comparisons when the accuracy performance doesn't match?
Is the root problem that ResNet-50 works best with a smaller batch size?
And how do you do meaningful research into new DNNs if there's always an "Maybe if I ran it again over there I'd get better results" factor?
Thank you.
It's great that there is now wider choice of (pre-trained?) models formulated for mixed-precision training.
When I was comparing Titan V (~V100) and 1080ti 5 months ago, I was only able to get 90% increase in forward-pass speed for Titan V (same batch-size), even with mixed-precision. And that was for an attention-heavy model, where I expected Titan V to show its best. Admittedly, I was able to use almost double the batch-size on Titan V, when doing mixed-precision. And Titan V draws half the power of 1080ti too :)
At the end my conclusion was: I am not a researcher, I am a practitioner - I want to do transfer learning or just use existing pre-trained models - without tweaking them. For that, tensor cores give no benefit.
After 59 days of playing with it, I sent it back (initiated return on 30th day, after I already figured out it doesn't live up to the hype, then had another 30 days to actually send it back).
With $3,000 I can buy 4 1080ti's, while only two are necessary to beat Titan V (in Titan V's best game). I only bought one though. NowInStock.net helped with buying 1080ti directly from Nvidia.
Yes, thanks for mentioning that! That's what the article is alluding to at the end. There's also something like a "cost-to-model" and that's influenced by how easy it is to make efficient use of the performance and how much tweaking it needs. It's also influenced by the framework you use... However, that's difficult to compare and almost impossible to measure.
AMD will enter the game soon once they get their software working, Intel will follow.
I suspect that Nvidia will respond with its own specialized machine learning and inference chips to match the cost/performance ratio. As long as Nvidia can maintain high manufacturing volumes and small performance edge, they can still make good profits.
But the TPUs are half the cost per this article?
Plus Google does the entire stack and can better optimize the hardware versus Nvidia. So it seem Google can improve faster I would think.
If there ever was a huge advantage doing the entire stack it is with neural networks.
A perfect example is Google new speech doing 16k samples a second with a NN.
https://cloudplatform.googleblog.com/2018/03/introducing-Clo...
Do not think Google could offer this service as a competitive cost without the TPUs.
This new method is replacing the method that was far less compute intensive so to offer at a competitive price requires lowering compute cost which suspect is only possible with the TPUs.
Consider that Google has some of the best machine learning researchers, compiler engineers, hardware engineers, and infrastructure in the business working on this.
It is why so many of the break throughs have come from Google. Great example is winning at Go almost a decade earlier than anyone thought possible.
They probably two of the strongest teams with one the Brain team and then the Deepmind team. But all the other engineers and infrastructure is first rate at Google.
Really at this point do not think the $100B cash is as important as Google already built the team and now experinced resources are far more difficult to get.
The other advantage for Google is their ability to attract the top engineers in addition.
Google just got started a lot earlier on all of this.
Plus benefits not having the baggage that Nvidia would have.
But never going to be able to use a TPU for graphics.
In the end it is about results.
By the way, the Tesla cards have no graphics output, so I'm sure why you'd say they have graphics baggage.
Baggage is a company thing. Google really has been an AI company since in the late 90s when Larry Page was asked about using AI to improve search and he replied he was using search to make AI happen.
Ha! When you amortize you are still spending money and you saying this really bothers me and is such a problem.
Too many look at things like you do and why companies get into problems. Capitalizing is not magic.
BTW, Google is also going to be able to iterate much quicker as the AI breakthroughs happen and come out with new versions that should stay well ahead of Nvidia.
The dynamics of the chip business have changed. Use to be companies bought chips from someone and then put them in to servers and sold the servers.
The problem is the company making the chips are NOT running the chips and do not have any skin in the game or the data needed to improve.
Now we have companies like Google making the chips and also running the chips and why we see power footprint being the focus far more than the past.
We will see all the big operations including Amazon make their own chips more and more.
A perfect example if Capsule networks replacing some uses of CNNs. Google with Hinton developed the Capsule network approach and will be supporting it far faster then you will see from Nvidia.
Then there is the canonical framework for AI being TF.
All of this was theoretical advantageous for Google and now we get to see they appear to be real with the pricing of the TPUs being about half of the cost of using Nvidia.
You seemed to have completely missed why Nvidia's stock has gone up 17x in 4 years while google only 3x. The dynamics of the chip business have not changed; you are focusing on a single market, DNN, which is a small piece of the entire science/engineering community. Google made a chip that accelerates DNN. They also chose not to make an API to use that hardware with outside TF. So if you could buy a tpu and put it in your own server, it would beat the V100 in performance/watt. You can't do that, so nvidia wins, because I can buy a V100, and in 51 days the price I bought it for ($8K) has already been burned through in GCP. If you need me to do the math to help you realize that now your only recurring cost on the v100 (power) is more than 100x less than the TPU, I can do that for you. But hopefully you understand now that the TPU is for a niche market outside of google, and it will never be a large source of revenue for them at $6.50/hour.
TF is not exclusive to google. Nvidia has engineers working on TF.
Your capsule example is again extremely poor. You think google can respin an asic quicker than nvidia? Not only does history say the exact opposite, but they both use TSMC.
Not sure the market cap or the P/E are apples to apples there.
Also:
> https://www.cnbc.com/2018/02/23/secretive-chinese-bitcoin-mi...
Find the questioning on the entire stack just baffling with Google.
People - Google has the strongest team of AI experts in the industry by a wide margin. At NIPS this year Google had more papers excepted than anyone else. The big AI breakthroughs come from Google. They solved Go a decade earlier than anyone thought possible. Would put FB #2 with AI experts but a very distant #2.
Google miles ahead with SDC.
Plus Google is able to attract the top talent better than anyone else.
https://unsupervisedmethods.com/nips-accepted-papers-stats-2...
Applications - Search, Photos, Speech, AlphaZero, Self Driving Cars, Google now has over 4k NN in production. Nobody else even in the ball park. Hand down the leader in applications.
Infrastructure - Tensor Flow now has 98k stars on GitHub. It is the canonical AI framework in the industry and really nothing else close. CNTK is #2 with 14k stars. But Google ads about 7x per days stars.
https://github.com/tensorflow/tensorflow
Then there is Google cloud infrastructure and their other engineering talents which are well ahead of anyone.
I can go on but this is so incredibly silly. There is little question that Google is leading at every layer of the AI stack by a wide margin.
This is so silly I suspect something else going on here. We do not seem to be discussing things based on reality.
Is this about Damore?
BTW, Nvidia can only spin up what they know about. Google does not share everything but luckily they do a lot for Nvidia.
In 2018 you just have to run the infrastructure to be long term viable in the chip game.
Edit: and there are FAR more cuda users in general than tensorflow if you're trying to compare apples and oranges.
Look a capsule networks and dynamic routing. That potentially drives a different architecture and Google has thousands of production models to use to optimize that Nvidia just does not have.
Plus it is one company so no IP issues.
But the biggy is we can see half the cost.
People - Google has the strongest team of AI experts in the industry by a wide margin. At NIPS this year Google had more papers excepted than anyone else. The big AI breakthroughs come from Google. They solved Go a decade earlier than anyone thought possible. Would put FB #2 with AI experts but a very distant #2.
Google miles ahead with SDC.
Plus Google is able to attract the top talent better than anyone else.
https://unsupervisedmethods.com/nips-accepted-papers-stats-2....
Applications - Search, Photos, Speech, AlphaZero, Self Driving Cars, Google now has over 4k NN in production. Nobody else even in the ball park. Hand down the leader in applications.
Infrastructure - Tensor Flow now has 98k stars on GitHub. It is the canonical AI framework in the industry and really nothing else close. CNTK is #2 with 14k stars. But Google ads about 7x per days stars.
https://github.com/tensorflow/tensorflow
Then there is Google cloud infrastructure and their other engineering talents which are well ahead of anyone.
I can go on but this is so incredibly silly. There is little question that Google is leading at every layer of the AI stack by a wide margin.
This is so silly I suspect something else going on here. We do not seem to be discussing things based on reality.
Is this about Damore?
BTW, Nvidia can only spin up what they know about. Google does not share everything but luckily they do a lot for Nvidia.
In 2018 you just have to run the infrastructure to be long term viable in the chip game.
What we have here is same work and half the cost. Which makes sense as Google does all the layers of the stack and is just has a fundmental advantage to better optimize.
AI it is even more important.
Google creating their own silicon which is far cheaper for them to run gets you 1/2 the cost of using Nvidia. Seems like an easy decision.
Help me? Seems like you are trying to hurt me? Just curious what drives it? Does not seem rational but driven my some emotion?
https://www.theverge.com/circuitbreaker/2016/5/19/11716818/g... Google reveals the mysterious custom hardware that powers AlphaGo
Exactly. Nvidia can match the performance already without 100% specialized processor. It's the just the price they need to cut by optimizing their architecture for tensor processing and reducing their profits when competition emerges.
Google is not in the business of becoming a major chip maker or competing with Nvidia head on. Putting hundreds of millions into new microarchitecture every second year eats lots of resources. They just want competitive market and the prices to go down.
Can't you just buy some 1080s for cheaper than this. I understand there is electricity and hosting costs, but cloud computing seems expensive compared to buying equipment.
So the restriction applies to the 1080ti but _not_ the titan V. I completely agree the restriction is total bullshit but it's important to get the facts straight.
It applies to both GeForce and Titan.
http://www.nvidia.com/content/DriverDownload-March2009/licen...
Thanks for the tip!
Sidenote: Love the illustrations that accompany most of your blog posts, are they drawn by an in-house artist/designer?
The illustrations are from an artist/designer we contract from time to time. I agree, his work is awesome!
Kudos to them; they are awesome!
That said, they fixed this on NVSwitch so it's just another HW hiccup like int8 was on Pascal.
You have price per hour and performance per second. Thus that ratio is not performance per image per $, you need to scale that. Also, the metric is not "images per second per $", but just "images per $".
EDIT: I found [1] which describes "tensor cores", "vector/matrix units" and HBM interfaces. The design sounds similar in concept to GPUs. Maybe they don't have or need interpolation hw or other GPU features?
https://supercomputersfordl2017.github.io/Presentations/Imag... http://learningsys.org/nips17/assets/slides/dean-nips17.pdf
For the last version of the TPU, Google provided more detail, e.g., in this paper:
https://arxiv.org/pdf/1704.04760.pdf
Hopefully, Google will publish something similar for TPUv2, but I have no knowledge whether or when that might happen.
Suspect we will need a gen 3 to get a paper on the gen 2.
Here is the gen 1 paper and highly recommend. Pretty interesting using 65536 very simple cores.
Definitely, no need to do any kind of rasterization here.
I wonder whether NVLink would make any difference for Resnet-50. Does anyone know whether these implementations require any inter-GPU communication?
Because Intel was involved in its development and made a number of tweaks to improve performance.
Be curious if it actually was significant or not.
A bit odd that the TPUs are provisioned on such a weaker machine compared to the V100s, especially when there were comparisons which included augmentation and other processing outside of the TPU.