Greetings, and welcome to the QuickLogic Corporation Acquire SensiML Conference Call. At this time, all participants are in a listen only mode. A brief question and As a reminder, this conference is being recorded. I would now like to turn the conference over to Mariah Shelton with Investor Relations. Please go ahead, Mariah.
Thank you, Rob. Welcome everyone, and thank you for joining us this morning. For a discussion of QuickLogic's acquisition of SensiML Corporation. With us today are Brian Faith, President and Chief Executive Officer Doctor. Su Chang, Chief Financial Officer and Chris Rogers, CEO of SensiML Corporation.
Before we begin, I will read a short Safe Harbor statement Some of the comments QuickLogic makes today are forward looking statements that involve risks and uncertainties, including but not limited to, stated expectations relating to revenue from new and mature products, statements pertaining to QuickLogic's defined activity and its ability to convert new design opportunities into production shipments, timing and market acceptance of its customers' products, statements regarding its future stock performance, schedule changes and projected projection start dates that could impact the timing of shipments, statements regarding the expected benefits or costs from any acquisition, and expected results and financial expectations for revenue, gross margin, operating expenses, profitability and cash. These statements should be considered in conjunction with the cautionary warnings that appear in QuickLogic's SEC filings. For additional information, please refer to the company's SEC filings posted on its website and the SEC's website. Investors are cautioned that all forward looking statements in this call involve risks and uncertainties, and that future events may differ materially from those statements made. For more details of the risks, uncertainties and assumptions, please refer to those discussed under the heading Risk Factors and the annual report on Form 10 K for the fiscal year ended December 31, 2017, the company filed with the SEC on March 9, 2018.
These forward looking statements are made as of today. The day of the conference call. And management undertakes no obligation to revise or publicly release any revision of the forward looking statements in light of any new information or hotspot, its corporate Twitter account, Facebook page, and LinkedIn page as channels of distribution of information about its products, its planned financial and other announcements, its attendance at upcoming investor and industry conferences, and other matters. Such information may be deemed material information, quick logic may use these channels to comply with its disclosure obligations under Regulation FD. This conference call is open to all and is being webcast live.
At this time, it is my pleasure to turn the call over to Brian Faith, President and CEO of QuickLogic. Please go ahead, Brian.
Thank you, Ryan, and thanks, everyone, for joining our call this morning. We're excited to be talking about this transaction with you. And I hope by the end of the call, you'll be as excited as we are about what this means for QuickLogic and what this means for you as investors. As you can see from the slide here on the transaction highlights, we're announcing the acquisition of SensiML. They're a software as a servicer SaaS AI company.
They are U. S. Based. They're based in Portland, and they're a provider of end to end software that allows OEMs to develop pattern recognition. Sensor algorithms using machine learning technology.
And Chris Rogers, who is also on this call, will be going into much more detail about SensiML in their technology. So he can share those details with you in a moment. The consideration for the transaction was all stock. And while we were not required to have this call due the, below the threshold of materiality from a company point of view. We wanted to have this call with you because we do feel like this is a very strategic event for QuickLogic total.
And I think again, you'll be pretty pleased to see the outcome of this by the end. So the benefits are somewhat are pretty obvious. We do hope that this is going to have a target positive EBITDA of the business unit for fiscal 2019. And we do think that this will significantly increase the served available market that we have as a company. If you think about this, the business that they're bringing is a SaaS software business and that we do not have that today as quick logic.
So adding that to our revenue streams is going to increase our served available market. And I think more importantly, there's going to be some cross leverage between their SensiML software suite, our quick AI platforms, and QuickLogic EFPGA IP or hardware based IP. And I'll get to that more in a second. Let's go for a little context now though on AI processing. So if you look at data center and cloud, that's really driven a lot of the revenue recently, from some of the high performance large, expensive FPGAs that are really optimized for speed.
And people that fall into this category would be the Alterra Business Unit of Intel and Xilinx. And this is really all about performance, all about compute. And one of the reasons why the FPGAs are used in the data center cloud is because for a lot of the AI or data, computing applications, people recognize that you need to change the algorithms or want to change the algorithms in the future. And so programmable logic and reprogrammable logic is a great way of doing parallel computing at scale with the ability to reprogram that in the future. And cloud ACI is going to continue to drive that growth of those FPGAs.
Now interestingly enough, if you go to entire opposite end of the spectrum to the edge of the endpoint, you still have a desire now to have more compute residing as close as possible to the sensor for this concept of localized AI and FPGAs are still a very good technology to do those types of accelerators that you need to do the local AI. But the big difference is that while in the cloud, you have sort of infinite power and cost budgets at the edge of the endpoint, don't have that, you have very restrictive cost and power budgets. And so if you think about deploying solutions at scale in that market, the edge and endpoint market, you have to have cost effective low power silicon. You also have to have software that allows the masses to take their ideas and implement those, and go to production. And that was one of the very interesting things that we learned about as we partnered with SensiML for the better part of the year now is that their analytic toolkit is really designed very well to help people accomplish that to size the algorithms and be mindful of the, the resource trained applications or processors that are used in the embedded world.
So, that was one interesting point. The second is that the fact that they can take advantage of hardware Chris is going to talk more about that in his slides. But the fact that they can make use of embedded FPGA and these platforms really make sense and another interesting reason why we really wanted to partner with these guys for the long term. So we feel pretty confident that there's a multibillion dollar opportunity for companies that can actually deliver this so called practical end to end solution for localized So one of the points I mentioned in the early slides was this notion of cross leverage of the Fullstack solution. And as we've been talking with customers and partners for the better part of the year now about our QuickAI, it became very clear that a lot of the masses of customers do want a full stack solution.
They want they want a processor, they want reference designs, they want software that allows them to take their ideas and realize it in a system and go to production. There's very few companies that have the financial resources of Apple and Google and Facebook and those big platform companies And there's actually a lot more volume in those, those other companies to bring to market. And we realized that as we're going out and doing side by side selling with Chris and his team at SensiML. So one of the interesting things that came out of that is this notion of cross leverage. So you can imagine that when we go out and sell to customers directly with SensiML, we're providing a full stack solution software to hardware.
But there's also a lot of customers that Chris was already selling at with SensiML directly. And as you'll see from his slides, they've actually ported their solution to other microcontrollers, as well as quick logic. So the cross leverage notion is that as they get more users of the SensiML software, more of these other processor companies may realize the value that can be delivered through having on chip hardware accelerators like embedded FPGA that can then drive more demand for the hardware FPGA, the more platforms that are available for them to optimize their software for. And the fact that SensiML software is actually designed to be aware of the platform it runs on, that creates this nice cross leverage and virtuous cycle between the 2. So the fact that, both of these will now be sort of under the QuickLogic domain, I think, is a wonderful thing for QuickLogic.
It's a wonderful thing for the market, and it's a wonderful thing for investors. At this point, I'm gonna turn it over to Chris so that he can give you a better sense of SensiML, and then I'll come back and and wrap it up at the end. So pass the ball. To Chris.
Great. Thank you very much, Brian. I think to your list, I would add that this is a wonderful thing, also for SensiML. I, we are, myself and, the rest of the SensiML team is very excited by, the opportunities brought by the disannouncement today and the fact that, we have a very shared vision, for, where AI the overall system can go. We complement very well in terms of software and hardware.
So, this is very exciting for us. So, first, before I get into the details, a little bit of background on who SensiML is, the team, myself, and the other, core developers that are part of the team, we have a, a team that's comprised of data scientists, firmware developers and software developers are all originated as an intact team out of, Intel Corporation. The genesis of this was back in 2012. Intel was making a foray into, heterogeneous core, microcontrollers for IoT and wearables. They had a group called the new devices group that was, very much, focused on consumer wearable devices and, targeting the developers of those devices with an end to end solution that comprised of hardware and software.
I led that software team and, our goal with this was, to really democratize the process of creating algorithms for, endpoint devices in a way that could make those devices truly intelligent. And, you know, the, the reality then and still today was that, the intelligent endpoint devices were by and large done by highly resource teams that had, lots of expertise in data science, in firmware development, and, and in coding so that, they could translate to give an application into practice and something that could fit within the device using the tools that are available today. We spend a lot of time sort of taking, you know, the expertise within Intel and trying to codify the process for creating intelligent algorithms that fit on, resource constrained and power optimized devices. Into a software tool that makes that readily accessible and practical to many users. So, If you look at AI, historically, most of the, computation for, AI takes place in the cloud, right?
So data centers and, cloud centralized approaches to, acquiring, you know, sort of big data problem sets and then analyzing those, are great for sort of traditional AI workloads. But when you start applying AI to IoT applications, in many cases, those applications are real time applications. And, the latency and performance characteristics of running that in the cloud just aren't practical, for what needs to take place. So you've seen in recent years a trend towards, shifting, centralized cloud processing to the Edge. And to date, a lot of the Edge analytics that are taking place are the same deep learning types of approaches run on relatively high end hardware, but they push more towards the edge of the network itself.
The missed opportunity to date so far is sort of the the underwater portion of the iceberg here, which is the billions of endpoint devices that, you know, can't use AI in the same manner that is being applied, to a high end, resource intensive computing devices today. But, you know, have a lot to contribute in terms of, processing locally and, making applications much more scalable the advantages of enabling these devices are that, in, in many cases, you can get the kinds of insight that you're looking for directly on the device itself, thereby eliminating a lot of the, network latency involved lowering the power requirements of the device, which is counterintuitive because if you're doing the processing on the device, you would think that would consume more power. But in fact, A large amount of the power budget is spent, in the case of battery powered wireless devices, just transmitting lots of data. If you can do the processing locally and just transmit the insights, you not only reduce your net power budget for the endpoint device itself and extend battery life and make possible battery powered sensors. But now you also can look at other network options that weren't really practical like putting, rich sensors of video and audio and, high frequency data on networks, like cellular IoT networks that have, long range, but relatively modest bid rates.
So, The problem with this is that, while these are all, great opportunities in the endpoint space, the challenges that building, AI algorithms that run on these devices is, no simple task. Is witnessed by the lack of software tools that are available today that make it practical for a developer to go take, you know, a data set and develop an algorithm that can run-in a power efficient way on these devices. If you look at the market, I think there's a general recognition, even in the business press, there's been articles recently about, the, you know, big opportunity for AI is not so much in the cloud these days. It's in the edge. Here's some, reference from Forbes, talking about the next gold mine is in, in the edge, Another reference here talks about the opportunity on embedded IoT devices for AI approaching 26,000,000,000 in 5 years.
So there's a general acknowledgement within the market, that edge and endpoint will be the growth space for AI. The challenge here, is, you know, as I said, not only, the need be able to create highly optimized code that can run on these resource constrained and powered budget devices, but also the expertise that's required. So this data here shows, if you were to compare and contrast, the data science, skill sets that are available versus those of general software and application developers. You know, this data comes from the U S Bureau Labor Statistics and it shows, you know, 28,000 data scientists available, most of which are consumed with, you know, sort of traditional cloud based, applications. You've got 100, just under 200,000 users who have data science skills, but aren't data scientists per se, and compare and contrast that against, you know, the 1,600,000 application and software developers out there that, you know, if they had the capability to build, intelligent devices, could take advantage of it.
So there's a real constraint here in terms of the bottleneck being the access to, skilled expertise to do these tools hand coding. So by contrast, what SensiML does is it embodies the process of, that expertise into a standardized workflow and a toolkit where, a developer of, modest understanding of AI or machine learning can take a data set that they create for their own application, collect it, choose their target endpoint device processor and then submit data sets and, training metadata into the sense of tool. And the tool will optimize and generate the firmware that, will provide an inferencing algorithm for their particular application. And it, it is a process that, not only democratizes the access to many more users, but also greatly accelerates the process. When we talk to developers that were doing this the hand coded way, they would spend 6 months in an effort building this code, and validating the code, optimizing it to fit within their appropriate, device.
Whereas with the SensiML Toolkit, we know from the outset what the target processor is because the user selects their desired device. And, you know, take the case of the quick AI processor. We know that device has a DSP. We know it has FBGA and, CPU. So the code that gets developed from the SensiML toolkit utilizes those capabilities and knows what, compute resources are, and it won't build a model that can't fit on the device.
So, yeah, that's a huge time savings in terms of the iterative process that's normally required. So in the context of, you know, the overall market for Edge And Endpointai, you We think of this, our world view of this is that there's 4 major sectors here. You know, starting at the very high end, you've got the autonomous driving ADAS and VRAR applications. And these are the things that, you're seeing, press from things like the Google surflow processor unit, you know, inshells move videos, Nvidia, these types of things. Moving from there down into the smartphone applications, as a sensor hubs or AI, co processors within phone platform.
We're purposely focused on, you know, the underserved space, within industrial and consumer IoT devices and bringing this kind of intelligence and capability to, a very much underserved space of microcontrollers that are capable, provided they have sufficient tools. If you look at the corresponding market TAM for each of these sectors, you can see that, you know, while the ADAS base is, you know, quite fascinating in terms of unit volumes, it's fairly nominal, right. Smartphones is a sizable market, but relatively flat. But the growth opportunities in industrial and consumer are predicted to be huge, you know, by, the next 5 years up to 8,000,000,000 units. And, you know, granted not all of these units are valid, you know, addressable market, is the way we think of it is that you've got roughly 2 sorts of that market that uses 32 bit microcontrollers.
And then you've got the things that are using sort of commodity devices below that like elevator controllers and these types of banks. They don't really even necessarily need that kind of complexity. But the majority of this market is moving towards more intelligence and, can benefit greatly from providing that at the edge opposed to, you know, centralized. So just some example applications here, that just show you the breadth of opportunity, that can be addressed. This isn't a vertical solution for any one market.
It's a common tool flow that can be used across many different, verticals. And if we look at, our focus is predominantly within either consumer devices or industrial IoT, you know, you get a flavor from this, here of the variety of opportunities that, we've seen and are engaging on, active customer projects things like industrial wearables, where we've created, motion and gesture sensing devices for first responders process automation, project we've done recently in fleet maintenance where we can do predictive you know, fault detection for, vehicles on chassis issues and wheel and tire issues. Sports and fitness, for sure. We had a lot of history there with Intel on creating intelligent, sports and prosumer devices. But then you look at, you know, areas and then smart home and then smart city, there's just lots of opportunity for, taking sensor data and doing much more with it, by making it practical to do that insights at the edge as opposed to having to send lots and lots of data, to a centralized location, which just doesn't scale when we when we're talking about billions of devices.
So, for SensiML's business model, how we make money is in freeways. Brian mentioned that we're a SaaS company. And so predominantly, we make money by providing developers with a toolkit on a subscription basis that lets them take their data and very quickly, turn that to developed and validated algorithms that they can run on their device. You know, a typical design here would be in the mid to high tens of 1000 of dollars per year for access to that service, which is still a very attractive value proposition into the amount of effort and labor and time that it takes them to do it through the traditional means. The next layer on, you know, how we monetize this is in licensing of the generated code.
So at the point that you've got, an algorithm that you're happy with from a development license standpoint, then, when you're ready to commercialize, we monetize the resulting code on a per unit basis. As a license. And that again is on the mid to high tens of 1000 of dollars per year for, resulting code on an average design win. And the other aspect of this is that on an ongoing basis, then you've got continuous learning. So it's not a 1 and done thing.
And this is what's really compelling is that at the point that you've shipped a device over the entire life cycle of that device, you have the opportunity to provide model updates, that continue to add value, and it's a way that the downstream customers can monetize their products by providing services on top of the hardware that they ship. So when you combine all three of these things, we see, this is very much a design win business, as a software corollary to, to QuickLogic's hardware business, with low to mid 100 of 1000 of dollars, per endpoint times 1000 of design wins that we can go after. And then, you know, as I said, yeah, the we see the complimentary visions between QuickLogic and our, with SensiML as, a very exciting thing, to show this sort of architecturally where we see the compliment is that, SensiML provides sort of a common layer for, rapidly building, the firmware that can take full advantage of the hardware's capabilities. And in QuickLogic, that's the, multiple different cores, the flexible fusion engine, the FPGA, the neuromorphic memory that's in the quick AI module. And, you know, by leveraging all of those things and exposing them to users in a way that's simple and straightforward for them to take advantage of, It's not just an advantage, for SensiML to monetize, but it also is an accelerator for QuickLogic's hardware business.
As we look at enabling third parties, the embedded FPGA business is an opportunity for us to take the learning from the PGA libraries we build for QuickAI and make those available and expose them for Arctic Pro and, provide that a means for, 3rd party SOCs to take advantage of this as well. And then, the ongoing support for 3rd party platforms as a whole, provides us with the breadth as well as the credibility as a true software agnostic companies that, our customers have grown to trust and will continue to trust as we'll support a variety of different platforms. So we're, yeah, I think we see lots of opportunities here to complement and the fact we are now integrated fully within QuickLogic provides us, the insights and capability to really take advantage of the latest and greatest hardware capability and expose it tool. Okay. So at this point, I'll turn it back to Brian.
As we go to this, this closing slide here, I want to reiterate some of the key points for our investors. I think one thing to note is that, you know, over the last year and a half or so, we've been talking a lot publicly about making our software platform that runs on the EOS S3 and open framework so that we can engage with a lot of the application software companies that can deliver the full solution to the market. And I think that we've talked a lot about sensory and DSP concepts around the void space. And SensiML is another example of a company that we were able to work with because of that. The openness of the platform allows to have them run their software intuitively on top of our platform.
I think that it's important to note that as Chris mentioned, they already have their toolkit running on other microcontrollers like, like Nordic and STMicro, which I think is wonderful. Because if you look at those companies, they already serve a large part of the microcontroller of VLE space that's in the IoT area. And we're going to continue to expand that as we move forward. And I think an interesting distinction there again is this notion of cost leverage. So the more platforms their software is running on, the more customers that are using it, is that virtuous cycle of cross leverage to drive more demand for the hardware acceleration capabilities that we have with our EFPGA as well as being able to run their same software on the QuickAI platforms now and in the future.
As I discussed earlier, we're not going to share financials beyond the point of saying that it's target positive EBITDA for the year. For their business unit. So any Q and A questions, please hold off financial related questions for the earnings call in February. We'll be able to discuss in more detail on that. And I'll just close by saying that as we've again, as we've been partnering with SensiML over this last year, it's really clear that we are aligned on this strategic vision of democratizing the technology, making it available to the masses, which is going to have that increased served available market.
You can do the math on what Chris's business model slide was and see that that's probably adding multiple 100 of 1,000,000 of dollars of available market to QuickLogic now with the SaaS revenue stream. So significant increase from being a device and an IP licensing company. And that's that notion of cross leverage. As we've gone through and met with customers, it's very clear their technology works customers really like the fact that it's easy to use and very quick to get to a workable model that they can test in the market. And, I think most importantly, there's a strong cultural fit between us and SensiML.
When you are selling next to each other on planes and in hotels, you get to know the person And I can say that there's a strong cultural fit between QuickLogic and SensiML. So really pleased to have him as part of the team and we're looking forward to doing some great things together. I'll close by saying that I think hopefully you can appreciate now after hearing about the context and more from SensiML that this really is the practical end to end solution that the market is looking for for this underserved edge and endpoint space. And looking forward to some great results as a result of this. So I'm also close by saying that both Chris and I will be at CS.
So if any of the investors or analysts on the call are going to be at CES, we'd be happy to show you some demos and products and look forward to that. After the call, we'll be, I guess, the next time we'll be touching base as investors will be the February earnings conference call. So, thank you for joining. And we'll open the call for questions at this
Thank you. First question is from the line of Suji Desilva with Roth Capital. Please proceed with your questions.
Hi, Brian. Hi, Sue. And welcome, Chris. Congratulations to all in the deal.
Thanks, Eddie. Thank you.
So, I know you don't talk about specifics about SensiML's revenue, but can you talk about maybe the end market breakout, of course, for your business so we can get a sense of which end market segments you've had success in, if that's something you'd look at?
Yeah, I could say that, of the markets, refocus, on consumer. Industrial IoT and and some in automotive. So I would say predominantly the first two We've had some opportunities in, automotive. We've purposely not gone after things like medical devices and, some of the other, smaller verticals.
Any breakout, Chris, between consumer and industrial just roughly to understand where you've gotten traction initially?
Yeah, it's been, about 2 thirds in industrial and about a third in consumer.
That's helpful. Thank you. And then, I know you talked about the landscape in broad strokes. Can you talk about who you guys compete with directly or if you don't think about what you do that way?
There are relatively few tools out there that are in this space. There is one emerging company called the reality AI, that's been there. And then, there's another, forming company called the X NAR AI. But when you look at things like TensorFlow or Cafe, those are deep learning tools that, really don't apply to endpoints. The other 2, I mentioned, in the case of Reality AI, they provide a portion of the, solution, but they don't go to the level of taking a to, optimized for FPGA and the DSP functions and, bringing it down to, you know, packaged firmware so that, you know, we provide the assurance to the user that, you know, when they generate the model within the tool, they know it's going to fit on the device.
So that is, a big time savings for them in terms of iterative process versus just getting a theoretical model that may or may not fit.
Okay, helpful as well. And then this question perhaps for Sue, on the balance sheet, the recent revolver you announced and, what's the expected cash flow impact of bringing Sensible into the fold and talk about your funding position kind of pre and post this if you're drawing down the revolver so forth. As backstop? I know it's a stock deal, but any color there would be helpful. If you want to wait till the earnings call for that, you can let me know as well.
Yeah. Yes. Actually, from a cash point of view, Suji, we're fine. With this additional revolving line that will keep us, full, above working capital lease. Again, the transaction is a stock pure stock purchase.
So, really doesn't have much impact on our cash usage, other than add, few engineers.
And can you comment whether the deal will be accretive to the cash flow or not or if it's totally that bad discussion?
So as Brian, mentioned that we expect the EBITDA positive by endoftheyear. So you can see that by end of the year should be neutral.
Okay, great. All right. I'll pass it along.
Thank you. Thanks, Suji.
The next question is from the line of Gary Mobley with Benchmark Company. Please proceed with your questions.
Bryan, Sue and Chris. Thanks for taking my question. Happy New Year.
Happy New Year.
I wanted to clarify on this SaaS model. When you basically license the toolkit under a SaaS model, wherever the arrangement $10,000 a year plus follow on licensing. Who are you licensing to the system OEMs you the microcontrollers, are you licensing to the MCUs as a tool that they can utilize to sell to their customers?
Our customers are the OEMs that are building devices using the hardware. So, it's the developers that are actually, creating products, using, microcontrollers. And just to clarify, on your point, the 10,000, it's like tens of 1000 of dollars. Because, you know, the value there is significant relative to the, effort and labor that's required to do hand coatings. So, there's, quite a bit more revenue there.
Sure. 90,000 is better than 10,000 a year. Thanks for the clarification. So With respect to, that point and as a follow on, is that, is it the main reason why you think you can to maintain the relationship with other silicon providers is the fact that you're licensing to the end customers versus, licensing specifically with STMicro or Nordic. And that's why quick under QuickLogic's umbrella, you can remain neutral or the Switzerland as it relates to algorithms?
Yes, that's right. That's right.
And as a fall into that, for Brian, you've been partnered with SensiML for a while, a while, why not just maintain that partnership and forge a deeper partnership? Why did you feel a need to acquire the company?
Because we felt like we for a few reasons Gary, I'll enumerate them. So firstly, we know that with the software revenue that we're talking about here with SensiML that going back to the slides, we were talking about potentially 100 of 1,000,000 of dollars in SaaS revenue. I would rather that be a quick logic independently. I think that's better for our investors and better for QuickLogic clearly. The secondly point is that, again, going back to the EFPGA, We see the value of embedded FPGA as this hardware acceleration capability to reduce power and free up Nips on processors.
I think that we're starting to see other companies or institutions like ETH gravitate to that no as well because of the test shift that we're doing with them. And I know deep down that once we have, people like some some really targeting and optimizing their tool to take advantage of that, that will create this cross leverage where that will help drive more business for embedded FPGA as a hardware accelerator into these other companies that have processors out in the market. So I think having both of this under one company with that shared vision, that's going to help realize that much, much more, than just running as independent companies. And I'll say that the last thing too and this is also very important. And I think maybe counterintuitive for some people is that we're not we're not doing this, this acquisition so that we can shut down SensiML's software business on these other processors.
We absolutely want them to continue to support these other platforms that they're already on and expand that. And I think that there's always this danger in the market today where if you have a really good software company that another company decides to acquire them, and then they will not have that same view as us. They will say, no, I want it only running on my chip and anything else now is not going to get any support. And that would be devastating for a company like us because we have such good traction in that shared vision, and we don't want that to happen. So the net of that is that it makes total sense for this to be under the same roof, knowing that we have the shared vision of having them continue that notion of democratizing the technology while being able to optimize it for platforms that we have or technologies that we can license other people.
Gotcha. Okay. How many employees in total?
We have 6 employees total.
And can you at least share with us whether or not you've generated revenue at this point?
Yes. We have. Okay.
I think that's going to do it for me, but congrats on the acquisition. It's a seemingly a good fit for QuickLogic.
Our next question is from the line of Richard Shannon with Craig Hallum. Please proceed with your questions.
Hi, Brian and Sue and Chris. Good morning and congratulations on the what looks like a very exciting deal. You know, most of my questions have been answered, but I want to follow-up on one regarding the competitive environments. And, Chris, your response to that, mentioned a couple of companies, one of which I, I know sort of. But, and I don't know the other one.
Try to look it up real quick, and it didn't seem to me that either of them have a an integrated, platform that can, that, seems to deal, you know, appropriately with a wide range of hardware platforms. And obviously, the optimization problem is difficult across a much broader array. So maybe you could address to the extent to which those other competitive platforms can do that to get us a sense of the, competitive dynamics, that'd be great.
Yeah. I mean, that's exactly it. We we strove from the outset to make this an end to end solution that, could integrate with,
standard eval kits, because
as you walk through the design win process, most of the OEMs start with the concept. They're going through a proof of concept phase. And they need something to rapidly prototype. So the fact that we can take standard eval kits, integrate that with our software from the data collection stand point, generate models that target the SoC that, that, eval kit is built around, and then, provide the firmware as well as a test and validation tool that allows them to have some confidence in, the resulting code. I think one of the pushbacks on machine learning and AI as a whole is that, you know, the developers tend to fear black box approaches that they don't know whether they supports are not when problems arise.
So validation is the equally important part of this. It's it's, I need to know how this works, not just, you know, assume that it's magic and it does, right? So, we, we've made sort of a holistic, approach to, addressing OEM build needs from years of experience of being in the space of creating devices and reference devices for customers from the Intel Day. And, so that's, I think one of the big highlights that we have, relative to the other competitors.
Okay. That's helpful, Chris. Thanks for that. And my other question, I guess mostly for probably Brian, if I understood your, your, comments earlier in the call regarding the, the opportunities to weave in your embedded FPGA IP. One of the opportunities here is not just, with the platforms that you've announced in your, in your press release, quick logic, the ARM based ones and Intel ones, but possibly, you know, internal SOCs that some might create to be enabled by your embedded FPGA technology.
If I could ask you to get your crystal ball up, Brian, and look out 2 to 3 to 5 years out. How much you how much of the hardware do you think is, is gonna be, one of these standardized forms you've listed in your PR versus ones that are SoCs and potentially enabled by your embedded FPGA.
That really is a crystal ball question, Richard. I think that, I mean, from a revenue point of view in the next couple of years. Clearly, it's going to be from platforms that are available, especially if you look at these markets that we're talking about. There's definitely some things on our roadmap that I think would be key platforms for this to be integrated and optimized for that would be out in that time horizon you're talking about. And I also feel pretty strongly that one of the reasons why we were working with ETH over this past year is this notion that once they flip that, that concept public, that test chip public that we've we've blogged about, that's going to drive more interest in the FPGA or EFPJ being a hardware accelerator that I think SensiML's tool can easily port to that will also drive follow on.
So I guess in terms of number of platforms 3 or 3, 5 years out, probably more platforms targeting all of the EFPJ and the SensiML software than clearly today. More than 50% just because of the fan out effect that we've been cultivating. Nearer than that from a revenue point of view, clearly, it's going to be our own platforms. And I would say announced devices that we've already got public and then other things that we have cooking that are not necessarily requiring new silicon to add bring more value to the market that are not announced yet.
Okay. Fair enough. Very fast.
Just one other point on average, if you look at the PR today, I think one of the platforms that says in there is it to be announced. So stay tuned for that.
Yes, I'm sure we'll see that very soon. So I look forward to that as well. I think that's all my questions. So, thank you very much and congratulations on the deal.
Thanks, Richard. Thank you. Thank you. At this time, I will turn the floor back to Brian Faith for closing remarks.
Yeah, I'd just like
to close by saying thank you for joining today on short notice. I hope you're as excited as we are about what this deal can mean for quick logic in our investors. And I'll reiterate that we will be at CES next week, Sue Chris and I, as well as our CTO, Tim Sachs, and we'd be happy to have follow on meetings with anybody on the call, in our suite with our demos and customer products. So after that, we will talk to you folks in February. Thank you.
This concludes today's conference. You may disconnect your lines at this time. Thank you for your participation.