WEBVTT
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00:00:05.759All right.
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00:00:07.839Welcome to the podcast, everybody.
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00:00:12.000I'm super happy because we've got fresh news.
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00:00:13.359May not be totally fresh.
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00:00:17.199You may have seen some of it, but we're going to dive into why this is important news.
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00:00:19.039Uh, so my name is Eric Wright.
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00:00:22.399I'm the host of the SNEA Experts on Data podcast here.
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00:00:35.039Uh, and I'm excited to have two fantastic humans with me to talk about the MRAM Alliance, how this relates to SNEA, what's happened, what's happening.
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00:00:42.240Uh, and I guess to kick things off, uh, we'll just do a quick intro just to say who you are, then we'll jump into the subject matter.
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00:00:43.759We'll start with you, Jay.
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00:00:44.479Sure.
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00:00:46.399Uh so my name is Jay Metz.
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00:00:49.359I am the chair of the board of directors for SNEA.
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00:00:55.039And uh I've been doing this for uh for quite a while and basically all things storage.
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00:00:56.320Nice.
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00:01:04.640And Jack, let's uh for folks that are brand new to you, which hopefully they aren't because uh uh you got some fantastic stuff you've already shared in the world.
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00:01:07.680And uh so let's let's introduce you for folks that are new.
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00:01:08.560Sure.
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00:01:10.319Uh my name is Jack Gage.
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00:01:17.120I'm uh chairman of the board of UMEM, uh an MRAM-based uh chip company.
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00:01:23.920And I'm also managing director of HITEN, which is an advisory and investment uh firm.
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00:01:41.439Now, the interesting thing about all of the work that's gone on in standards, we've got incredible innovation, we've got the industry is struggling with what is the most starved resource today.
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00:01:47.680And certainly uh memory is uh top of that list, aside from dollars to purchase more memory.
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00:01:55.760But we quite literally have a lot of real problems in delivering on the supply that's needed to do stuff.
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00:02:11.520And also, as that's occurring, there's an incredible amount of innovation happening about how do we better optimize what we have in place, what other innovations do we make that can maybe defer the need or remove the need to go down sort of traditional memory growth.
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00:02:16.800Many, many things are impacting a lot of stuff in in the industry.
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00:02:35.759But what's great about seeing organizations that are standards-based, that have really, really good broad customers and clients who are contributing to those communities is now we're bringing more and more people with a common goal of making systems better so that we can make people better.
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00:02:48.719And uh, Jay, if you want to introduce what is it that we want to talk about today, how what is the MRAM alliance in relation to SNEA now, and why is this important to SNEA and its community members?
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00:02:50.000Oh, absolutely.
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00:03:02.159So um one of the things that we're trying to do inside of SNEA is uh look for an end-to-end holistic way of approaching problems that we're dealing with in the data centers now.
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00:03:07.680So obviously we're a storage uh ecosystem uh standards organization.
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00:03:25.919And so we've developed an awful lot of uh standards that people have they they use inside of their um, you know, inside of the data centers, they use inside of their laptops, we use inside of uh in tribes, uh different technologies that deal with uh data movement, data protection, and so on.
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00:03:36.479So um it made perfect sense that once the you know the MRAM group was uh does its development inside of JEDIC, which is another standards organization.
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00:03:45.680Um but because of the fact that it's a persistent memory solution, it made perfect sense to fit in with some of the other things that we're doing inside of SNEA.
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00:04:00.960One of the the initiative that we're creating is something called uh uh storage AI, which is the approach for how do we handle all of the storage responsibilities that go across the board for AI-based workloads.
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00:04:10.960That's not the only thing we do, but that is one of the things that is incredibly important to a lot of our members as well as the general conversation going on across the planet.
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00:04:16.560And so new memory technologies, especially persistent memory technologies, fit in perfectly.
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00:04:26.160So we're we're looking to try to take stuff that is going on inside of MRAM and extrapolate that into other areas that it's related to.
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00:04:37.279And I guess that's probably why it's important for us to learn what it is that made the MRAM Alliance so interesting and such a fantastic fit.
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00:04:40.879So, Jack, I'd like to actually start from the the first principles.
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00:04:49.680What is the problem that that the org is you know here to solve and and how does it relate in in the work that you've been doing?
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00:04:51.439Yeah.
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00:05:03.519Um well, first of all, the uh memory wall, as it's now uh more widely known, has been in existence for a while.
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00:05:07.759You know, um five plus years, if not more.
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00:05:16.720Since 2012, the computer's been growing at a rate of a thousand X since 2012.
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00:05:20.639Memory has only grown 30x.
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00:05:31.040So there is a huge deficiency between the rate of innovation in the memory and the rate of innovation in the computer.
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00:05:46.160So more than ever now, companies need to consider any new types of memories and architecture as well as interfacing in order to tear down that memory wall.
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00:05:55.759Um and there MRAM is a is ideally suited for uh AI inferencing.
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00:06:04.319Um I can tell you later on, I've got eight reasons why MRAM is ideally suited for uh AI inferencing.
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00:06:38.160Uh so you know, all of this made that we felt we needed a broader audience to get beyond the existing uh uh memory companies that have been doing pretty much the same thing and improving it uh year over year, but with the same technology, and then be able to open to the world a new technology, again, that's ideally suited for AI inferencing.
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00:07:02.879Well, it's an interesting thing because it's a in effect that memory wall creates a Jevons paradox that we have seen a lot of stuff that's moved into changes and innovations around KV caching and how do we better do sort of like storage tier offload where we can still keep things intact but just move it down to lower tiers.
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00:07:14.000But then again, even in the memory space, how do we better, you know, build memory and target memory capabilities based on these new workloads?
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00:07:22.160And inference fundamentally shifted everything because you know, I I I don't want to be like every LLM out there.
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00:07:23.839The the paradigm has shifted, yeah.
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00:07:35.519Like, but we have actually seen that inference is everywhere and it's very unpredictable with how it's being consumed, and it will be for a long time.
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00:07:44.560So this is why you know you must be excited because you were already the world arrived to where you are, Jack.
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00:07:48.000Like, like, like, you know, Sans and Jay, you've probably seen this as well with a lot of things.
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00:07:58.879Like, we've been developing these ideas, and all of a sudden people are like, hey, you know, how do we actually look to what's out there today and and put it into the place in the most optimal way?
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00:08:01.279So what do you see as like now the sync?
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00:08:24.800You know, we we started this group uh three years prior to joining SNEA um as a small group and growing a little bit, but we felt we needed a bigger platform, which we are you know extremely happy since uh you know we joined SNEA in January uh was the broading.
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00:08:27.519Uh and that's what we're looking for.
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00:08:50.399We're looking for ways to reach out to system companies, we're looking for ways to get more collaboration, because at the end of the day, more collaboration, the market is huge, uh, and more collaboration will yield uh a better solution, better system solution uh for those AI uh you know memory issues.
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00:08:53.840You had me at collaboration, Jay.
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00:08:58.159You've you and I have been at this for a long time as well, through various communities.
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00:09:05.600So what do you what do you see as like kind of the immediate win with creating this and creating the SIG?
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00:09:22.080And also across the other parts of the SNEA community, where do you see the things plugging in that will both help what Jack and the SIG are doing as well as bring that this whole ecosystem together?
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00:09:45.919So I think that if you're looking at the life cycle of a workload, and you know, from a very practical perspective, especially for those of us who are geeks and we love to figure out how things work, um you hear people talk about things like time to first token, you know, uh you hear people talk about uh direct access into storage and accelerated this and accelerated that.
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00:09:56.159But what winds up happening is that you you run the risk of focusing on a very small part of a larger puzzle with a lot of moving parts.
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00:10:06.639I believe Jack um kind of uh mentioned it, but I want to reinforce the fact that a lot of these things are are not done in isolation.
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00:10:10.799You know, we we have we have the data movement that goes from one place to the other.
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00:10:21.840It's not it's not just that you have an accelerator or a processor, it's not just that you have memory, it's not just that you have KV cache and and whatever buzzword happens to be popular in the next, you know, the next month or so.
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00:10:25.120It has to do with how all these things relate together, right?
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00:10:32.720And we we see ourselves with a number of different alternatives that are coming out because there are limitations at these kinds of scale.
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00:10:37.120And I don't think that people necessarily understand exactly what kind of scale we're talking about.
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00:10:50.879You know, um in in certain AI workloads, for instance, it is not uncommon to talk about small, medium, and large size clusters, but people have no clue what that actually means, right?
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00:10:59.120So for in in my day-to-day job, for instance, where I am, you know, we consider a medium-sized cluster to be 100,000 GPUs.
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00:11:00.879And that's just the GPUs.
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00:11:09.679That's not counting everything that goes along with it, you know, the storage, the networking, the real estate space you have to put in in place for it, right?
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00:11:17.440Um and these kinds of workloads are very, very, very demanding, but they're also extremely sensitive, right?
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00:11:26.080So you have to put in a lot of protection, you have to put in a lot of of security in the right places, and there's so many different things to make the whole thing work.
00:11:26.240 -->
00:11:32.480It's it's sort of like saying, well, I'm gonna remove a single lug nut from my car and expect it to work right.
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00:11:34.879And that's just not a very wise thing to do.
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00:11:37.840Well, it's the same thing inside of these data centers, inside of its workload.
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00:11:40.080And AI is only one workload, right?
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00:11:57.679So what we're trying to do is say, look, as we start to figure out, you know, how do we get a better, you know, a better piece of the puzzle, where does it fit into the overall, you know, uh the overall ecosystem, overall, you know, technology architecture, you know, there are consequences, there are trade-offs that have to be made.
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00:12:13.279And what we do at SNEA is we look at those consequences and we work with other organizations like JEDEC and a few others to make sure that this actually is a smooth transition from you know a more traditional data center type of environment to a very workload-specific environment.
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00:12:23.759So it's very important to us that we figure out um, you know, in advance so that people don't learn the hard way, you know, what the trade-offs are going to be and what they need to be.
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00:12:25.600Yeah, it was funny.
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00:12:39.759I was a in a DR discussion with somebody about uh a steward solution, and the question came up that I I hear that there's an issue with like kind of a full like rack row level failure, that that could cause a problem.
00:12:39.919 -->
00:12:50.320I said if you're uh an NCP or if you're uh a neo cloud and you lose a a whole row and you have no other backup for it, yes, that's a problem.
00:12:50.480 -->
00:12:59.360Like we the localized things that we're thinking about don't come into play the same way when you, as you said, like kind of scale it out.
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00:13:04.240Where we're not thinking about rack level and server U level.
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00:13:09.120We're talking about row scale and data center scale stuff.
00:13:09.279 -->
00:13:17.360So, you know, Jack, as you've been developing and and you know, looking at where these technologies apply and what are the use cases that match.
00:13:17.679 -->
00:13:38.559What is the what's what's in the MRAM Alliance portfolio of hardware tips and tricks, you know, that has created now what will be a really strong opportunity to build around stronger and faster yet at scale type of memory implementations.
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00:13:54.240Yeah, there um as Jay mentioned, there um you know, there's performance issues or performance metrics uh like time to force token and then uh you know token throughput.
00:13:54.559 -->
00:13:58.720There's also uh power related uh constraints.
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00:14:11.679Right now, those racks are just uh you know gobbling power, and you need uh uh close to a nuclear power plant to power the new data center.
00:14:11.759 -->
00:14:14.080So it's it's it's going crazy.
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00:14:28.639Um and you know, one of the two of the uh eight reasons MRAM is ideally suited for AI inferencing is one, it's fast, it's really fast, and low latency.
00:14:29.039 -->
00:14:33.360Uh it is much faster at a bit cell level than a DRAM.
00:14:34.080 -->
00:14:41.440And uh the latency because it's a direct read, just like an SRAM, uh, is really low.
00:14:41.519 -->
00:14:48.879So you can get latency on the first token less than 10 nanoseconds, uh, which is blazing fast.
00:14:49.360 -->
00:14:59.679And you can run that MRAM you know with with an engine, you can run it basically maxing out the process frequency.
00:15:00.000 -->
00:15:02.639And it doesn't matter which process it is.
00:15:02.720 -->
00:15:08.960So uh so you can get really fast throughput and uh and really low latency.
00:15:09.679 -->
00:15:16.480And also this memory is able to retain data uh without power.
00:15:17.039 -->
00:15:27.519So you can uh and you don't have any refresh, you don't have any of this overhead, so you can achieve uh a very low power with that.
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00:15:47.120So uh as Jim mentioned, uh we're totally aligned with with the SNEA uh even prior to joining SNEA, which is we want system companies to learn more so they can do a better job at fault and not learn by their mistakes.
00:15:47.279 -->
00:15:55.519We want we want to reduce the number of mistakes they're making and speed up their time to adoption and time to market.
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00:16:22.799Um and right right now we're embarking to um uh uh it's it's a new initiative we have, which is on the system design side to be able to, by application, explain and give guidelines to companies on how they can incorporate MRAM and uh you know what things they should be careful in doing their system design.
00:16:23.519 -->
00:16:36.639And you know, we'll expand that to webinars, we'll expand that to FAQs so uh we can get uh the word out and again, as Jay's saying, make people's life easier.
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00:17:11.920Yeah, it's great that you know I'd say the education is one of the most powerful things that we even if you go looking for individually, I may go dig and find some things and I'll individually learn some things, but inevitably when I get to SDC and you have extended conversations with other people who've also done their own research, then now you're actually working in that collaboration and there's a persistence in the collaboration versus me like I'm just gonna build one thing for my specific purpose and I'm in and out.
00:17:12.240 -->
00:17:37.039But being able to move this in and have that SIG approach means you get you know the right backing as far as governance and and everything to keep the the lights on for the org, but also just now you get access to all these fantastic technologists who are as excited as heck about this stuff as us, which you know maybe that tells you something about us, but I'd say I'm pretty damned excited about what the heck is in front of us.
00:17:37.200 -->
00:17:43.200Like especially when you get to low power, you know, we're seeing way more things around inference at the edge.
00:17:43.359 -->
00:17:44.559And we actually know what the edge is now.
00:17:44.640 -->
00:17:49.119It's not not what it was eight, 10, 12 years ago when somebody invented it.
00:17:49.200 -->
00:17:50.880And thank God we didn't land on fog.
00:17:52.640 -->
00:17:54.160But here we are.
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00:18:11.039Like we are legitimately seeing small footprint, small power footprint, inference capabilities that are gonna need this type of memory access and persistence because you solve one problem, you create 10 others.
00:18:11.119 -->
00:18:19.680Uh and so it sounds to me like bringing the eye close to the edge is also a power reduction because now you don't have to transmit as much.
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00:18:26.960And we know the the wired and wireless transmission, especially wireless transmission, uh tend to burn a lot of power.
00:18:27.039 -->
00:18:44.400So, Jay, because this is obviously certain we'll say we're AI focused in the discussion, but do you find is that like kind of the biggest conversation right now inside SNEA and with some of the like your your own like other community partners?
00:18:47.119 -->
00:18:47.680Yeah, yeah.
00:18:47.759 -->
00:18:54.880Like we obviously we're we're a bit hyper-focused on inference in in a few of the discussions we have, but I don't think that we're alone.
00:18:54.960 -->
00:19:00.640I think this is like a very strong collective problem we're challenging, we're trying to solve together.
00:19:01.279 -->
00:19:02.880Oh, I I I think you're right.
00:19:02.960 -->
00:19:10.000I mean there's it it would be disingenuous to say that AI didn't bring up a lot of the conversations.
00:19:10.640 -->
00:19:14.079Um however, I do find it is somewhat fractal.
00:19:14.559 -->
00:19:14.880Right.
00:19:15.039 -->
00:19:20.319So it's just it's just one stage amongst many in that in that level of discussion.
00:19:20.559 -->
00:19:33.119Um you know, ultimately what we're trying to figure out is is how to make data movement more efficient overall, period, regardless of whether it's for AI or something else.
00:19:33.359 -->
00:19:43.920Um and and the everything that goes around it, you know, everything that sort uh uh surrounds that question is fair game for conversation, right?
00:19:44.240 -->
00:19:55.839So so when you think about storage, and I and I believe that one of the big problems that we have is that a lot of people who are not storage oriented don't really understand what storage means, right?
00:19:56.000 -->
00:19:57.920And storage has one job.
00:19:58.160 -->
00:20:08.079Give me back the correct bit I asked you to hold on to for me when I ask for it, and everything that goes around that is all part of what we have to do.
00:20:08.240 -->
00:20:10.960Now, different people are gonna focus on different things, right?
00:20:11.279 -->
00:20:20.079Um, you know, if you if you're focusing on the capacity of a drive, you know, um the bit that I ask you to hold on to is what you're gonna remember.
00:20:20.400 -->
00:20:24.319If you're going to be an application person, you want to be the correct bit, right?
00:20:24.400 -->
00:20:26.880If you're an end user, it's when I ask for it.
00:20:27.200 -->
00:20:34.960But if you're happening to be, you know, a lot of the members inside of SIA, not only do they have to deal with all that, but it's give the back, right?
00:20:35.119 -->
00:20:36.480Give me back that bit.
00:20:36.799 -->
00:20:44.160And that word give is one of the things that is the most difficult to do correctly and efficiently.
00:20:44.400 -->
00:20:53.039Because every time you have to give, you you have to find the trade-off about accuracy and resiliency and security and reliability.
00:20:53.200 -->
00:20:57.759And the best bit that you have to send is the one you never sent, right?
00:20:58.079 -->
00:21:02.880So if I don't have to send an IO, then I am that's great.
00:21:03.039 -->
00:21:08.160But if what I'm doing is I'm just basically kicking the responsibility to someone else, not so much.
00:21:08.480 -->
00:21:18.640So it's important to note that we all have to take a look at all these different uh these these concentric circles of influence with the the technical architectures.
00:21:18.799 -->
00:21:25.359And every single stage along the way, you've got the physical aspect of it, you've got the networking aspect of it, you got the memory, and so on.
00:21:25.440 -->
00:21:32.559You know, it's like somebody standing on Times Square, yo, I got your MRM over here, and I got your VDR over here, and I got your gold watches right here.
00:21:32.799 -->
00:21:39.359So, so what we need to do is we need to say, all right, you know, how are all these things supposed to be working in concert together?
00:21:40.000 -->
00:21:51.440And that means that it used to be that SNEA did some of this, and NVMexpress did some of this, and OCP did some of this, and Jeddek did some of this, and DMTF did some of this.
00:21:51.680 -->
00:21:59.759It's not it's now the problems are so big and so involved that SNEA works on a large portion of it, JEDEC works on a large portion of it, OCP.
00:22:00.079 -->
00:22:01.680He works in a large portion of it.
00:22:02.000 -->
00:22:10.640So even something as focused as some as MRAM has implications beyond SMIA, beyond Jetic, in other areas.
00:22:10.960 -->
00:22:17.200And you need to be able to have the conversation at all of the different levels if you're going to have any credibility.
00:22:17.440 -->
00:22:17.680Right.
00:22:17.839 -->
00:22:18.160Right.
00:22:18.319 -->
00:22:21.519And so, you know, Jack's talking about wanting to get people involved in doing this.
00:22:21.680 -->
00:22:28.160He's like, yes, because of the fact that the the tail of influence is extremely long now.
00:22:28.400 -->
00:22:28.640Yeah.
00:22:28.799 -->
00:22:29.200Right.
00:22:29.440 -->
00:22:36.400And that's it's something that that if you're not careful, you could find yourself on the business end of a customer hissy fit.
00:22:36.480 -->
00:22:38.240And I don't think anybody really wants that.
00:22:38.799 -->
00:22:39.599This is true.
00:22:39.759 -->
00:22:40.000Yeah.
00:22:40.160 -->
00:22:58.960So Jack, uh with that, you know, what what is what are some of the other use cases that are maybe not necessarily AI centric, but like what are the more you know enterprise use cases and other use cases that you've already seen come out or that you are seeing as opportunities for for MRAM as a spec?
00:22:59.599 -->
00:23:00.240Yeah.
00:23:01.119 -->
00:23:03.839First of all, Jay is is is right on.
00:23:04.079 -->
00:23:06.960So um, you know, we're we're totally in line.
00:23:07.200 -->
00:23:08.160Can I have that in writing?
00:23:08.240 -->
00:23:10.559I want to I want to I want to pass that on to someone.
00:23:12.319 -->
00:23:12.960Yeah.
00:23:13.279 -->
00:23:28.000And you know, on the on the give difficulty to accurately and efficiently uh pass on the data, uh that is reason number three, MRAM is ideally suited for AI interesting.
00:23:28.480 -->
00:23:33.680It is one of the newer memory that is the most reliable.
00:23:34.000 -->
00:23:41.119Um so reliability is extremely good, and it's also uh uh very high endurance.
00:23:41.519 -->
00:23:53.920And so, you know, going to the uh question you asked about applications, it does have a wide range of applications because of that, uh, including automotive right right now.
00:23:54.000 -->
00:24:33.279You see uh companies, uh the big um automotive chip companies uh making AI-based uh sorry, automotive MROM-based chips, not only for AI, but for a wide range of applications, including microcontrollers, um staying on the on the reliability uh and also the fact that inherently, because MRAM is not a transistor-based technology, MRAM is uh sits between two metal layers on top of the active layer.
00:24:33.519 -->
00:24:41.119So um because of that, it is very resilient to uh uh radiation.
00:24:41.440 -->
00:24:57.359So when people are talking about uh data centers in space, uh it is a lot easier when you have a technology that is uh resilient to uh radiation and soft errors.
00:24:58.000 -->
00:25:00.960Uh but in terms of application, it's pretty wide.
00:25:01.119 -->
00:25:30.240I mean, it goes from you know smart cameras, and there are some right now being designed uh with uh with MRAM uh to um applications like ARVR, uh ultra low power application, because you can shut down the system, you know, say you have a smartwatch and you're sleeping, you don't need that memory necessarily to be uh to be live.
00:25:30.319 -->
00:25:42.079It's the your watch is not doing very much, but right now uh it's hard to shut it down because you don't have a memory that is fast and that retains the data.
00:25:42.240 -->
00:25:58.400So with this, you know, you can really play and be able to have different levels of um you know the uh of uh of sleep modes where you can gradually and gracefully uh lower the power.
00:25:58.640 -->
00:26:09.039But that scales all the way to data center because since this memory is uh is bling fast for inferencing, um you can scale that all the way to data centers.
00:26:10.559 -->
00:26:14.799Well, and we're seeing more of you know just the right patterns as we learn.
00:26:14.880 -->
00:26:22.640Yeah, we we couldn't necessarily have predicted how the right patterns and the storage patterns will go for these workloads.
00:26:22.960 -->
00:26:24.960And even as we go.
00:26:25.119 -->
00:26:45.920So definitely durability, the endurance of the actual hardware, the the fact that you can subject it to pretty harsh conditions, you know, that's again, these are things that they may have seen like unnecessary for most of the use cases we have two years ago, even you know, probably even even six months ago, it would have been like, oh, it's interesting.
00:26:46.160 -->
00:26:54.400But when you dig in and you look, just in in a few minutes, you've already hit yeah, this is a very, very important technology.
00:26:54.720 -->
00:26:56.480And uh so yeah, super exciting.
00:26:56.640 -->
00:26:57.920So, what do you see now?
00:26:58.079 -->
00:27:01.759What what's the best thing for folks that do want to get involved?
00:27:02.000 -->
00:27:03.759Uh Jack, I'll start with you.
00:27:03.839 -->
00:27:07.839What's the the way that you want to engage via the SIG?
00:27:08.160 -->
00:27:12.640And then uh Jay, I'll have you kind of close up and talk about how to connect.
00:27:13.440 -->
00:27:20.799Yeah, well, uh first of all, SNEA is the platform, and uh you can find us through the the SNEA platform.
00:27:21.039 -->
00:27:35.200Uh we have a booth, uh SNEA booth at the uh in the exhibit hall of uh FMS, which is going to happen uh early August, uh future of memory and storage.
00:27:35.680 -->
00:27:39.200And we will have demos at the SNEA booth.
00:27:39.519 -->
00:27:47.039Uh so you know if you want to learn more about what we're doing, I will give a talk uh at FMS.
00:27:47.200 -->
00:27:50.000We will have uh chat with the experts.
00:27:50.240 -->
00:27:57.359So that's a good forum if you have time uh during the day or Wednesday evening to learn more.
00:27:57.839 -->
00:28:07.039Uh and uh we will also be present with multiple talks at the uh SDC.
00:28:07.680 -->
00:28:11.279Uh and then later on this year we'll have the Global Forum.
00:28:11.839 -->
00:28:30.319So many ways to find out more about the MRAM Alliance and uh and how you can join and how you can get involved either on the uh overall alliance SIG or one of the SLEP SIG we have, uh subgroups.
00:28:30.400 -->
00:28:37.039Uh as I talked about earlier, the system design with MRAM, memory interfaces, that's another point.
00:28:37.119 -->
00:28:56.559Uh basically in this, you know, give back uh accurately and efficiently uh transfer the data back, uh, so memory interfaces so that we can play along because it's a multi-memory uh type system in order to optimize the architecture.
00:28:56.640 -->
00:29:04.400Uh and then we also have a roadmap track uh which kind of goes over what's coming in the future.
00:29:05.200 -->
00:29:05.839Fantastic.
00:29:05.920 -->
00:29:07.279Well, looking forward to FMS.
00:29:07.440 -->
00:29:08.319I'll be there.
00:29:08.400 -->
00:29:10.640Uh strangely enough, they allowed me to speak at it.
00:29:10.720 -->
00:29:15.279So I don't know how who they might have a low bar there, but uh so it's gonna be exciting.
00:29:15.359 -->
00:29:16.799And one can wait.
00:29:16.880 -->
00:29:18.640I'm uh I'm in that same boat.
00:29:20.640 -->
00:29:22.400So we can all enjoy.
00:29:22.559 -->
00:29:25.200We will be there to uh put you through the test, Jack.
00:29:25.359 -->
00:29:27.359We're gonna put you through the technical exam.
00:29:27.519 -->
00:29:38.319Uh but Jay, what what what's ahead and based on this and what what do you recommend for folks when you know how do they engage both with SNA and with Jack and the folks through this SIG?
00:29:38.880 -->
00:29:43.440Yeah, well, Jack um uh was absolutely spot on about the upcoming events.
00:29:43.519 -->
00:29:44.960We've got a number of things that are going on.
00:29:45.039 -->
00:29:56.960I mean, as we're doing this recording, um we've got the the FMS presentations that are coming up, we've got the SNEA Developer Conference in September, the AI infrastructure summit, the supercompute.
00:29:57.119 -->
00:29:58.880SNEA is going to be at all of these things.
00:29:58.960 -->
00:30:18.559But beyond that, um there's an you know, the ongoing information that we have at SNEA.org is incredibly important for people to be able to keep the updates and see the webinars that Jack was talking about and the uh in the integration that's going on, and to participate in the storage AI community is absolutely incredibly important.
00:30:18.880 -->
00:30:35.519Um the the key thing I think that is is really missing from a lot of the discussion, and I believe SNEA is stepping up for this, is that a lot of times people are just sort of saying, Well, this is what's going on, these main big companies are kind of pushing this on us.
00:30:35.680 -->
00:30:37.200I don't really have a voice.
00:30:37.440 -->
00:30:39.519This is the opportunity to have a voice.
00:30:39.759 -->
00:30:48.160This is the chance for people to say, you know what, I want to have some thoughts in in in the open discussion, and that's exactly what SNEA offers.
00:30:48.319 -->
00:30:55.039You have the opportunity to share your voice, share your ideas, and even influence the way that the direction works for the entire ecosystem.
00:30:55.279 -->
00:31:06.720And so by joining, you know, SNEA.org or SNEA.ai, if you want to be specifically for the AI stuff, both of those are great options, even beyond the events that are coming up in the next few months.
00:31:07.440 -->
00:31:21.839It's gonna be, you know, I think we're gonna see more and more of these smaller events and and smaller get-togethers because it's it's eventorama right now with all these AI use cases coming up.
00:31:21.920 -->
00:31:25.359So I'm glad to see fantastic minds coming together.
00:31:25.519 -->
00:31:29.119Looking forward to seeing both you both at FMS and at SDC.
00:31:29.359 -->
00:31:32.240And for folks, of course, you can check out the links below.
00:31:32.319 -->
00:31:38.240We've got links to, and if you just do a search for MRAM Alliance, the beauty part, it'll take you right to the main page.
00:31:38.319 -->
00:31:40.640It'll pull you into the SNEA org.
00:31:40.799 -->
00:31:41.440Get on in.
00:31:41.519 -->
00:31:45.279If you're not already a member of SNEA, then uh, well, you're a wrong person.
00:31:45.440 -->
00:31:46.319Go do that thing.
00:31:46.400 -->
00:31:48.559Go get in, go get in charge in SNEA.
00:31:48.799 -->
00:31:50.400There's a fantastic group of humans.
00:31:50.559 -->
00:31:52.480Uh and rare.
00:31:53.279 -->
00:31:58.079I have to say it is um also affordable as SNEA as if we're great.
00:31:58.400 -->
00:32:03.440So, of course, smaller code makes it very affordable to join SNEA.
00:32:04.160 -->
00:32:04.400Yeah.
00:32:04.640 -->
00:32:04.799Yeah.
00:32:04.960 -->
00:32:07.119And then it's very startup friendly.
00:32:07.759 -->
00:32:12.640It's super important because nowadays you know, the last thing you want is to join, you know.
00:32:12.799 -->
00:32:25.519I mean, there are obviously sort of very vendor-aligned things that end up having to happen to a lot of these hardware communities, and then next thing you know, they're burning their entirety of their budget just to be able to participate with an upstream you know player.
00:32:25.680 -->
00:32:31.839And that's tough, you know, where this is much more broad ecosystem coverage and international too.
00:32:31.920 -->
00:32:47.039So that's the other thing is you get these are there's regional events, there's community members from all over, and it doesn't end when SDC closes the the gate at the hotel, it continues online from that moment onwards.
00:32:47.119 -->
00:32:48.799So there's lots of ways to keep engaged.
00:32:48.880 -->
00:32:50.400So I'm I'm excited.
00:32:50.720 -->
00:32:51.200All right.
00:32:51.359 -->
00:32:54.079Well, so let's just do a last little quick recheck.
00:32:54.160 -->
00:32:57.839So, Jack, where do folks find you if they want to get connected with you?
00:32:58.079 -->
00:33:03.759And uh you can feel free to say at SNA.org is a great place to find you because I imagine there's a few places to go.
00:33:04.000 -->
00:33:08.799Yeah, I would say at SNA.org uh or SNA.ai.
00:33:09.200 -->
00:33:12.079That's probably the best place to find us.
00:33:12.240 -->
00:33:16.880Or you know, you can Google us or you can use AI to find us.
00:33:17.200 -->
00:33:18.000That's it.
00:33:22.240 -->
00:33:25.440It's always neat to be consumed by the very thing that we create.
00:33:25.599 -->
00:33:30.880Uh Jay, for for folks that want to uh get connected with you and uh how do they do it?
00:33:31.279 -->
00:33:45.440I think the probably the best way to do that is to um uh as as Jack said, you know, um I can be reached through SNEA.org, but also on LinkedIn and the usual social media challenge at channel chat channels, excuse me.
00:33:45.680 -->
00:33:47.200Um, you know, YouTube.
00:33:47.599 -->
00:33:54.319I've got um I uh I'm starting to get back into doing some more blogging on my my own site, jmets.com.
00:33:54.400 -->
00:34:00.960So you'll see some other things in a variety of different places, but um I'm sure you'll probably hear from me somehow.
00:34:01.279 -->
00:34:18.719And also uh for folks that didn't get a chance to see it, Jay did a really great presentation on sort of the some of the stuff that SNE is handling at uh Tech Field Day, which is uh uh a sort of a vendor, a non-vendor vendor-ish community of practitioners, but it was a great presentation.
00:34:18.800 -->
00:34:19.679It's available online.
00:34:19.760 -->
00:34:25.360So if you just do a search for Jay Met's uh Tech Field Day, you'll find a lot of content.
00:34:25.440 -->
00:34:26.719Look for the most recent one.
00:34:26.800 -->
00:34:31.119Uh so that was a great presentation, really kind of diving into the problem.
00:34:31.519 -->
00:34:40.880And that that reminded me that as smart as all of those amazing humans are that we're surrounded by, we forget that things have changed.
00:34:41.119 -->
00:34:46.400And we have this sort of like assumption that everybody just is caught up on how AI consumes storage.
00:34:46.639 -->
00:34:59.599And it was wild to see some of the questions that I wouldn't have expected from really, really super smart people that have been in the industry a long time going, oh, right, we we need to keep educating and we need to keep learning.
00:34:59.840 -->
00:35:02.880So uh I'm looking forward to learning from from both of you.
00:35:03.039 -->
00:35:04.880So, Jack, Jay, thank you very much.
00:35:05.039 -->
00:35:15.920And uh for folks, of course, again, follow the links, get involved with SNEA, and uh we'll see you all on the next Experts on Data podcast and live at FMS and at SDC.
00:35:17.920 -->
00:35:18.719Thank you.
00:35:19.039 -->
00:35:19.679Bye.
