QuickLogic Corporation (NASDAQ:QUIK) SensiML Corporation Merger Call January 4, 2019 11:00 AM ET
Executives
Moriah Shilton – Investor Relations
Brian Faith – President and Chief Executive Officer
Chris Rogers – Chief Executive Officer, SensiML Corporation
Sue Cheung – Vice President of Finance and Chief Financial Officer
Analysts
Suji Desilva – ROTH Capital Partners
Gary Mobley – The Benchmark Company LLC
Richard Shannon – Craig-Hallum Capital Group LLC
Operator
Greetings, and welcome to the QuickLogic Corporation Acquires SensiML Conference Call. At this time, all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. [Operator Instructions] As a reminder, this conference is being recorded.
I’d now like to turn the conference over to Moriah Shilton with Investor Relations. Please go ahead, Moriah.
Moriah Shilton
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, Dr. Sue Cheung, 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 production start date 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 the statements made. For more details of the risks, uncertainties and assumption, please refer to those discussed under the heading Risk Factors in 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 future events.
Please note, QuickLogic uses its website, the company’s blog QuickLogic 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, and QuickLogic 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’s my pleasure to turn the call over to Brian Faith, President and CEO of QuickLogic. Please go ahead, Brian.
Brian Faith
Thank you, Moriah, 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 Service or SaaS AI Company. They are U.S.-based are 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 and 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 to 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 in 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 targeted – sorry, excuse me, 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 the SaaS software business and that we do not have that today as QuickLogic. 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 QuickAI 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 extensive FPGAs that are really optimized for speed. And people who fall into this category would be the Altera 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 and the 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 program logic and reprogram logic is a great way of doing parallel computing at scale with the ability to reprogram that in the future. And cloud-based AI is going to continue to drive that growth of those FPGAs.
Now interestingly enough if you go to the 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 to localize AI.
But the big difference is that, while in the cloud you have sort of infinite power and cost budgets. At the edge and the endpoint, you 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 analytics toolkit is really designed very well to help people accomplish that to size the algorithms and be mindful of the resource-constrained applications or processors that are used in the embedded world. So that was one interesting point.
The second is the fact that they can take advantage of hardware accelerators. And Chris is going to talk more about that in his slides. But the fact that they can make use of embedded FPGA in 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 is a multibillion dollar opportunity for companies that can actually deliver this so-called practical end-to-end solution for localized AI and that’s really what helped drive this transaction.
So one of the points I mentioned in the early slides was this notion of cross-leverage of the full stack 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 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 other companies to bring to market. And we realized that as we are 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 is also a lot of customers that Chris is already selling out with SensiML directly. And as you’ll see from his slides, they’ve actually ported their solution to other microcontrollers as well as QuickLogic.
So the cross-leverage notion is that, as they get more users at 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 IP that we provide as QuickLogic. And the more platforms that are out there, silicon devices they have embedded 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 two. So the fact that both of these will now be sort of under the QuickLogic demand, 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 going to turn it over to Chris, so that he can give you a better sense of SensiML. And then I’ll come back and wrap it up at the end. So pass the ball to Chris.
Chris Rogers
Great. Thank you very much, Brian. I think to your list, I would add that this is a wonderful thing also for SensiML. We’re, myself and the rest of the SensiML team is very excited by the opportunities brought by the – this announcement today. And the fact that we have a very shared vision for where AI and 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 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 4A into heterogeneous core microcontrollers for IoT and wearables that 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’s comprised of hardware and software.
I lead the 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 the reality then and still today was that, the intelligent endpoint devices were by and large done by highly resourced teams that had lots of expertise in data science, in firmware development and in coding, so that they could translate to a given application into practice and something that could fit within the device using the tools that are available today.
We spent a lot of time towards taking the expertise within Intel and trying to codify the process for creating intelligent algorithms that fit on our 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 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 underwater portion of the iceberg here, which is the billions of endpoint devices that can’t use AI in the same manner that is being applied to high-end resource-intensive computing devices today. But 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 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.
So 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 a long range, but relatively modest bit rates.
So the problem with this is that, while these are all great opportunities in the endpoint space, the challenge is that building AI algorithms that run on these devices is no simple task. It is witnessed by the lack of software tools that are available today that make it practical for a developer to go take a dataset 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 has been articles recently about the big opportunity for AI is not so much in the cloud these days, it’s in the edge. Here is some reference from Forbes talking about the next goldmine is in the edge. Another reference here talks about the opportunity embedded IoT devices for AI approaching $26 billion in five years.
So there’s a general acknowledgement within the market that end – edge and the endpoint will be the growth space for AI. The challenge here is, as I said, not only that need to be able to create highly optimized code they can run on these resource-constrained and power 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, this data comes from the U.S. Bureau of Labor Statistics and it shows 28,000 data scientists available, most of which are consumed with 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 the 1.6 million application software developers out there that if they have 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 with 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 dataset that they create for their own application, collect it and choose their target endpoint device processor and then submit as data sets and training metadata into the SensiML tool. And the tool will optimize and generate the firmware that will provide an inferencing algorithm for their particular application.
And 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 we’re doing this the hand-coded way, they would spend six months in an effort building this code and validating the code and optimizing it to fit within the 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 take the case of the QuickAI processor, we know that device has a DSP. We know it has a FPGA and a CPU. So the code that gets developed from the SensiML toolkit utilizes those capabilities and knows what the compute resources are, and it won’t build a model that can’t fit on the device. So that’s a huge time savings in terms of the iterative process that’s normally required.
So in the context of the overall market for edge and endpoint AI, we think of this – our worldview of this is that there’s four major sectors here. Starting at the very high-end, you’ve got the autonomous driving ADAS and VR/AR applications. And these are the things that you’re seeing press from things like Google TensorFlow Processor Unit, Intel’s Movidius, NVIDIA, these types of things.
Moving from there down into the smartphone applications as sensor hubs or AI co-processors within the phone platform, we’re purposely focused on 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 while the ADAS space is 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 by the next five years up to 8 billion units.
And granted not all of these units are valid addressable markets is the way we think of it is that, you’ve got roughly two-thirds 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 on these types of things. 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 not at the edge as opposed to centralizing.
So just some example of 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 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 fault detection for vehicles on chassis issues and wheel and tire issues.
Sports and fitness, for sure, we have a lot of history there with Intel on creating intelligent sports and prosumer devices. But then you look at areas within smart home and then smart cities, there’s just lots of opportunity for taking sensor data and doing much more with it by making it practical to do that kind of insight at the edge as opposed to having this on 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 business model, how we make money is in three ways. Brian mentioned that we are 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 into developed and validated algorithm that they can run on their devices.
A typical design here would be in the mid to high tens of thousands of dollars per year for access to that service, which is still a very attractive value proposition relative to the amount of effort and labor and time that it takes them to do it through the traditional means.
The next layer on 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 thousands of dollars per year for the 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 one and done thing. And this is what’s really compelling is that at the point that you’ve shipped the device over the entire lifecycle of that device, you have the opportunity to provide model updates. They 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 QuickLogic’s hardware business with low to mid hundreds of thousands of dollars per endpoint times, thousands of design wins that we can go after.
And then, as I said, the – we see the complementary visions between QuickLogic and with SensiML is a very exciting thing to show the sort of architecturally, where we see the complement 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 QuickAI module. And by leveraging all of those things and exposing them to users in a way that 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 learnings from the FPGA libraries we built for QuickAI and make those available and expose them for ArcticPro and provide that as a means for third-party SoCs to take advantage of this as well.
And then the ongoing support for third-party platforms as a whole provides us with the breadth, as well as the credibility as a true software agnostic company that our customers have grown to trust and will continue to trust as we’ll support a variety of different platforms.
So we’re – yes. I think, we see lots of opportunities here to complement. And the fact that, we’re 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 in the tool.
Okay. So at this point, I’ll turn it back to Brian.
Brian Faith
Thanks, Chris. So as I go to this closing slide here, I want to reiterate some of the key points for our investors. I think one thing to note is that, 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 voice space, and SensiML is another example of a company that we’re able to work with because of the openness of the platform allows us to have them run their software and to really on top of our platform.
I think it’s important to note that, as Chris mentioned, they already have their toolkit running on other microcontrollers like Nordic and ST Micro, which I think is wonderful, because if you look at those companies, they already serve a large part of the microcontroller BOE space that’s in the IoT area, and we’re going to continue to expand that as we move forward.
And I think the interesting distinction again is this notion of cross-leverage. So the more platforms that 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 in – 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&A questions, please hold off financial-related questions for the earnings call in February, where 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 imparting with SensiML over this last year, it’s really clear that we are aligned on the 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’ business model slide was and see that that’s probably having multiple hundreds of millions of dollars of available market to QuickLogic now with the SaaS revenue stream. So significant increase from just being a device in 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 and customers really like the fact that it’s easy to use and very quick to get to a workable model that they can test to – 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 them 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 intent 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’ll also close by saying that, both Chris and I will be at CES, 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. And after the call, we’ll be – I guess, the next time we’ll be touching base with investors will be the February earnings conference call.
So thank you for joining, and we’ll open the call for questions at this point.
Question-and-Answer Session
Operator
Thank you. We’ll now be conducting a question-and-answer session. [Operator Instructions] Thank you. The first question is from the line of Suji Desilva with ROTH Capital. Please proceed with your question.
Suji Desilva
Hi, Brian. Hi, Sue, and welcome, Chris. Congratulations to all on the deal.
Brian Faith
Thanks, Suji.
Chris Rogers
Thank you.
Suji Desilva
So I know you don’t talk about specifics about SensiML’s revenue. But can you talk about maybe the end market breakout, Chris, for your business, so we can get a sense of which end market segments you’ve had success in, if that’s something you look at?
Chris Rogers
Yes. I could say that the markets we focus on consumer, industrial IoT and some in automotive. So I would say, predominantly, the first two, we’ve had some opportunities in automotive. We purposely not gone after things like medical devices and some of the other smaller verticals.
Suji Desilva
Any breakout, Chris, between consumer and industrials roughly to understand where you’ve gotten traction initially?
Chris Rogers
Yes. It’s been about two-thirds in industrial and about a third in consumer.
Suji Desilva
Okay, that’s helpful. Thank you. And then I know you talked about the landscape in broad structure. Can you talk about who you guys compete with directly, or if you don’t think about what you do that way?
Chris Rogers
There are relatively few tools out there that are in the space. There is one emerging company called the, Reality AI, that’s been there. And then there’s another forming company called the, Xnor 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 two 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 it to optimized for FPGA and the DSP functions and bringing it down to a packaged firmware.
So that we provide the assurance to the user that when they generate a model within the tool, they know what’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.
Suji Desilva
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 SensiML 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.
Sue Cheung
Yes. Actually from a cash point of this, Suji, we’re fine with this additional, our revolving line keep us flow above working capital needs. Again, the transaction as a stock, pure stock purchase. So really it doesn’t have much impact on our cash usage other than add a few engineers.
Suji Desilva
And can you comment whether the deal would be accretive to the cash flow or not, or it’s too early to have that discussion?
Sue Cheung
So, as Brian mentioned that, we expect that EBITDA positive by end of the year. So you can see that by end of the year, it should be neutral.
Suji Desilva
Okay, great. I’ll pass it along. Thank you.
Chris Rogers
Thanks, Suji.
Operator
The next question is from the line of Gary Mobley with Benchmark Company. Please proceed with your question.
Gary Mobley
Hi, Brian, Sue and Chris, thanks for taking my question.
Brian Faith
Hi, Gary.
Gary Mobley
Happy New Year.
Brian Faith
Happy New Year.
Sue Cheung
Happy New Year.
Gary Mobley
I wanted to clarify on this SaaS model. When you basically license the toolkit under a SaaS model, whatever the arrangement, $10,000 a year, plus follow-on licensing. Who are you licensing to the system OEMs using the microcontrollers? Are you licensing to the MCUs as a tool, they can utilize to sell to the customers?
Chris Rogers
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, in fact, tens of thousands of dollars, because the value there is significant relative to the effort in labor that’s required to do hand coding. So that is a quite a bit more revenue there.
Gary Mobley
Sure. If $90,000 there, then $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 continue to maintain the relationship with other Silicon providers is the fact that you’re licensing to the end customers versus licensing, specifically with ST Micro or Nordic and that’s why quick – under QuickLogic’s umbrella, you can remain neutral or the Switzerland as it relates to algorithms?
Chris Rogers
Yes, that’s right.
Gary Mobley
Okay. And as a follow on to that for Brian, you’ve been partnered with SensiML for a while. Why not just maintain that partnership and forge a deeper partnership, why didn’t you feel a need to acquire the company?
Brian Faith
Because we felt like we – for a few reasons, Gary, and 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 that you’re talking about potentially hundreds of millions of dollars in SaaS revenue. I would rather that be a QuickLogic revenue overall as opposed to just a third-party company with SensiML running 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 MIPS on processors. I think that we’re starting to see other companies or institutions like ETH gravitate to that notion as well because of the tests that we’re doing with them.
And I know deep down that once we have people like SensiML 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 the embedded FPGA as a hardware accelerator into these other companies that have processors out in the market.
So I think having both of us 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 would 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 doing this acquisition, so that we can shutdown 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 everything else now is not going to get any support. And I 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 our technologies that we can license to other people.
Gary Mobley
Gotcha. Okay. How many employees in total?
Brian Faith
Yes. We have six employees total.
Gary Mobley
Okay. And can you at least share with us whether or not you’ve generated revenue at this point?
Brian Faith
Yes, we have.
Gary Mobley
Okay. I think that’s going to do it for me, but congrats on the acquisition. It’s seemingly a good fit for QuickLogic.
Brian Faith
Thanks, Gary.
Sue Cheung
Thank you.
Operator
Our next question is from the line of Richard Shannon with Craig-Hallum. Please proceed with your question.
Richard Shannon
Hi, Brian and Sue. And Chris, good morning and congratulations on the – it looks like a very exciting deal.
Chris Rogers
Thank you.
Sue Cheung
Thanks, Rich.
Richard Shannon
Most of my questions have been answered. But I wanted to follow-up on one regarding the competitive environment. And Chris, your response to that, you mentioned a couple of companies, one of which I know sort of.
But – and I don’t know the other one, trying to look it up real quick and it didn’t seem to me that either of them have an integrated platform that can – that seems to deal 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 would be great.
Chris Rogers
Yes. I think that’s exactly it. 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 standpoint, generate models that target the SoC that, that Eval kits has 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 push backs on machine learning and AI as a whole is that, the developers tend to fear black-box approaches that they don’t know, whether they can support or not when problems arise. So the validation is equally important part of this, which is I need to know how this works, not just assume that it’s magic and it does, right?
So we’ve made sort of a holistic approach to addressing OEM build needs from years of experience of being in the face of creating devices and preference devices for customers from the Intel days. And so that’s, I think, one of the big highlights that we have relative to the other competitors.
Richard Shannon
Okay, that’s helpful, Chris. Thanks for that. And my other question, I guess, mostly for probably Brian. If I understood your comments earlier in the call regarding 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 press release, QuickLogic, the ARM-based ones and Intel ones, but possibly internal SoCs that somewhat might create to be enabled by embedded – your embedded FPGA technology.
So I could ask you to get your crystal ball, Brian, and look out two to three to five years out, how much of the hardware do you think is going to be one of these standardized platforms you listed in your PR versus ones that are SoC and potentially enabled by your embedded FPGA?
Chris Rogers
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 feels 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 concept public, that test public that we blogged about, that’s going to drive more interest in the FPGA or eFPGA 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 three or three, five years out, probably more platforms targeting all of the eFPGA and the SensiML software than clearly today, so more than 50% just because of the standout 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 these silicon to add, bring more value to the market that are not announced yet.
Richard Shannon
Okay, fair enough.
Brian Faith
Just one other point on that, Richard. If you look at the PR today, I think, one of the platforms it says in there is to be announced. So stay tuned for that.
Richard Shannon
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.
Brian Faith
Thanks, Richard.
Sue Cheung
Thank you.
Operator
Thank you. At this time, I’ll turn the floor back to Brian Faith for closing remarks.
Brian Faith
Yes. I just like to close by saying, thank you for joining today on the short notice. I hope you are as excited as we are about what this deal can mean for QuickLogic and our investors. And I’ll reiterate that we will be at CES next week, Sue, Chris and I, as well as our CTO, Tim Saxe, and we’d be happy to have a 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.
Operator
This concludes today’s conference. You may disconnect your lines at this time, and thank you for your participation.
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