>> Live from San Francisco, it's theCUBE, covering Micron Insight 2018. Brought to you by Micron. >> Welcome back to San Francisco everybody. You're watching theCUBE the leader in live tech coverage. I'm Dave Vellante. He's David Floyer, and we're covering Micro Insight'18. It's all about bringing together artificial intelligence and the memory and storage requirements. We're here on the embarcadero. We've got treasure island that way. We've got the financial district over there. We've got Golden Gate bridge behind us. Tom Eby is here as senior vice president and GM of Micron's booming compute and networking business unit. Good to see you Tom. >> Great to be here. >> And Permal Savla is here. He's the director of deep learning at NVIDIA. Welcome. >> Thank you. >> So obviously some of these new emerging work loads require collaboration between folks like Micron and folks like NVIDIA. But Tom why don't you kick it off. What are some of the big trends that you're seeing in some of these alternative work loads that's driving this collaboration? >> Well a lot of what we're talking about here today is the drive of AI and machine learning work loads, and the implications for memory. Certainly there's a host of them, natural language processing, photo and image recognition, applications in medical research, applications in optimizing manufacturing like we're doing in our fabs, and there's many many more. And of course what's exciting for us is that to support those in an optimized way really does require the mating of the optimal processing architecture, things like GPUs. With the right high band width with low latency memory and storage solutions. That's what leads to great partner ships between partnerships like Micron and NVIDIA. >> David was explaining at our open the intensity of the work loads that you guys are serving, and how much more resources that requires to actually deliver the type of performance. Maybe you could talk about some of the things that you're seeing in terms of these emerging work loads. >> Yes, so at NVIDIA, we build systems for X rated computing. AI and deep learning is a very quickly expanding field at this point which needs a lot of CP horse power. What we are seeing is that different applications like you said there's image processing, whether it's video, whether it's natural language processing the amount of data that is there, that is required to do deep learning and AI around it, we break it up into two work flows. One is the training where you actually train the software, and make it intelligent enough to then go and do inference later on. So that you can go and get you results out of it at the end of it. We concentrate on this entire workflow. That's where when we are looking at it from a training perspective, the GPU gives it the processing power. But at the same time all the other components around it perform at the peak. That's where the memory comes in. That's where the storage comes in, and we need to process that data very quickly. >> Yeah, so we know from system's design that you got to have a balanced system or else you're just going to push the bottle necks around. We've learned that over the years, but so it's more than just slapping on a bunch of storage and a bunch of memory. You're doing some other deeper integration, is that correct and what is that integration? >> Yeah, I think the two companies have had a great relationship, just to talk about a couple examples. We essentially co-defined a technology called GEDR 5X, which greatly enhanced the speed of graphics technology. We gently introduced that to the marketplace with NVIDIA about 18 months ago. And then worked with them again very closely on a technology called GDDR six, which is the next generation of even faster technology. We were their launch and ran partner for their recently announced G-force RTX line of cards. It's a very deeply engaged early in the process, define the process, define the standards, jointly develop the solution. Very intimate sharing in the supply chain area. It's a great relationship for us. We're excited about how we can continue to expand and extend that relationship by going forward. >> So obviously there's the two parts of it. You said the learning part of it, and the inference part of the computing. What do you think is the difference between the two? I mean obviously at the end of the day, the inference part is critical. That's got to be the fastest response time. You have to have that in real time. Can you talk a little bit about what you're doing to really speed that up, to make that micro seconds as opposed to milliseconds? >> So from an NVIDIA perspective we build the entire end to end tools steps for training and inferencing. We have a set of libraries that we have made it openly available for all of our customers, all our partners, and all users. So that they can go download it, and do the training so they can use the different frameworks and libraries to accelerate the work that they're doing. And then transform it onto the inference spot. We have something called denser RT, which is basically denser real time. That gives the capability to get these answers very quickly. So on our D4 of the tuning, Chip said that we just announced. We can get a very high performance for our image. So any kind of image recognition or image processing that we need to do, we can do that on the systems very quickly. And we can meet, rebuild entire architectures. So it's not just about one piece. It's about the whole end to end architecture of the system. >> So we heard earlier today in the analyst briefing, the press briefing that Micron certainly in the last 40 years has changed. We're seeing a lot more diversity. Usually it'd be all about PCs. Now there's just so many alternative work loads emerging. Clearly NVIDIA is playing there as well with alternative processing capabilities. What do you guys see as some of the more exciting, emerging work loads that are going to require continued collaboration and innovation? >> Yeah, well I think to build a little bit on some of the other comments about the need for real time inference, one of the things in the area of diversity that we've found interesting. The relationship between Micron and NVIDIA in high performance memory really started around their graphics business. But we are seeing in other markets closer to the edge, in automotive, in networking and in other areas where there's a need for that real time performance. Yet there's also a need for a degree of cost effectiveness. Perhaps a little more so than in the data center. That we're seeing technologies like GDR six being applied to a much broader range of applications like automotive, like networking, like Edge AI, to provide the performance to get that real time response but in a form factor and at a cost point that's affordable for the application. >> Anything you'd add to that Permal? >> So I would also add you talked about applications, different applications that are changing right? Today we announced a new set of libraries and tools for the analytic space. That's again a big work load in the enterprise data centers, that we are trying to optimize and accelerate with machine learning. So we announced a whole set of tools which take in these large data sets that are coming in, and applying it in the data centers and using it to get answers very quickly. So that's what NVIDIA is also doing is expanding on these capabilities as we go in. And as these components and as these technologies get better it just gets our answers much more quickly. >> As exacts in the space and you guys both, you're component manufacturers, and so you sell to people who sell to end consumers. How do you get your information in that sort of pull through? Obviously you work with your customers very closely. >> Mm-hm. >> How do you get visibility to their customers? Just going to go to shows, you go do joint sales calls, how does that all work? >> Certainly some of that is in discussions with our customers and their marketing groups about what they're seeing from a customer point of view. But certainly there's other paths. One of the reasons behind the hundred million dollar venture fund that we announced today, is one of the best ways to get that advanced insight, is to be working with some of the most innovative start ups that understand what some of those end users needs might be and are developing some unique technologies. So there's a range. Working with our customers through eventually finding others, but it's important that we understand those needs because the lead time to developing the solutions both memory and processing architectures is quite well. >> Of course everybody wants to work with NVIDIA, you guys have an inundated like come on oh no we're the most. We're tied up now. Of course there's not a lot of choices here when you're talking about the levels of components that you're selling. But what's life like at NVIDIA? I mean they've been knocking down your doors to do partnerships. >> I think we've grown from being just the component to now being a complete system and an architecture. We don't only just build just a chip that the GPU was. We also build full SLCs. We also build the libraries, software, and the tools that are required to make this complete end to end solutions. We also do a lot of open source technologies because we want our customers and our end cast partners to build and take what we have and go beyond what it's capable of. That's where we end value at the end of the day. Yes, it's all of us together. We need to work together to make that much more faster as we go. >> The tuning is incredibly important. This is complicated stuff. It doesn't just work out of the box, right? So you need an ecosystem as well. >> Yes. >> Yes. >> That's what you guys have been out building. Tom, well give your final thoughts. >> Yeah well I guess to build a little bit. Certainly NVIDIA is moving up the stack in terms of the ecosystem, the software, the complete solution and I think Micron does as well. Like you commented, traditionally it was a component play. And increasingly, we're going to be building subsystems in memory and storage that occurs today on the storage side. I think we'll increasingly see that in memory, and with some of the future, very promising technologies like 30 Cross Point. >> Yeah it's the dawn of the days where everybody just gets piece parts and put them all together. They need you you guys to do more integration, and more out of the box like you say subsystems. So guys thanks very much for coming on theCUBE. Really appreciate it. >> Thank you. >> Thank you. >> Alright you're welcome, keep it right there everybody. We'll be back in San Francisco, you're watching theCUBE from Micron Insight 2018, accelerate intelligence. We'll be right back after this short break. (music)