GSI Technology, Inc. (GSIT) on Q2 2022 Results - Earnings Call Transcript
Operator: Ladies and gentlemen, thank you for standing by. Welcome to GSI Technology's Second Quarter Fiscal 2022 Results Conference Call. . Before we begin today's call, the company has requested that I read the following safe harbor statement. The matters discussed in this conference call may include forward-looking statements regarding future events and the future performance of GSI Technology that involve risks and uncertainties that could cause actual results to differ materially from those anticipated. These risks and uncertainties are described in the company's Form 10-K filed with the Securities and Exchange Commission. Additionally, I have also been asked to advise you that this conference call is being recorded today, October 28, 2021, at the request of GSI Technology. Hosting the call today is Lee-Lean Shu, the company's Chairman, President and Chief Executive Officer. With him are Douglas Schirle, Chief Financial Officer; and Didier Lasserre, Vice President of Sales. I would now like to turn the conference over to Mr. Shu. Please go ahead, sir.
Lee-Lean Shu: Good afternoon, and thank you for joining us to review our fiscal second quarter 2022 financial results. Our revenue increased by 17% year-over-year for the second quarter of fiscal 2022 to $7.8 million compared to $6.7 million in the second quarter of fiscal 2021. At the high end of the guidance provided earlier in the second quarter through the first half of fiscal 2022, revenue is up 25% compared to fiscal 2021 and sales to Nokia, our largest SRAM customer, has stabilized despite ongoing supply chain challenges that are troubling our industry. We reduced our net loss by 22% year-over-year as a result of higher revenue and gross profit, and only a modest increase in operating expenses on a year-to-date basis. At quarter end, we had nearly $51 million in cash, cash equivalents and short-term investments to support the launch of new products and sales and marketing efforts to build a pipeline of opportunities. Our legacy SRAM business is profitable and generate cash flow to support the development and pending launch of our radiation-tolerant devices and the Gemini APU solutions. We continue to advance our key initiatives with ongoing APU and Rad-Tolerant opportunities. We are progressing in the current customer engagements for both categories and have had a few new beta customer engagement for the APU. We continue to prioritize the allocation of our capital towards the APU, which we believe is central to the new, to the long-term value of the company, given the unique opportunity for all our technology. We are committed to our strategic investment in APU. To our own detailed analysis of market data, we estimate that the global TAM for APU search application is approximately $137 billion. Later in the call, Didier will discuss our assumption and the market represented to illustrate in more detail our conviction regarding this strategic investment. We were recently selected to be an organizer and judge for the building scale approximate nearest labor search challenge, the first of its kind competition in large scale approximate U.S. labor search, on ANS. The event is being hosted by NeurIPS as part of its annual conference on rural information processing systems. GSI is one of the members of a panel led by Microsoft that includes AI thought leaders from industry and academia. Participating teams submission will be evaluated against challenged datasets containing at least 1 billion vector vehicles. The winning teams will be launched at the NeurIPS 2021 conference in December. Recent advance in A&S techniques for search, recommendations and ranking requires supporting a building, treating all large datasets. This competition is to help establish a consensus on which algorithms are effective at this scale. ANS is important to search, receivable and recommendation. A challenge for ANS algorithm designers is to create a data structure that enables fast receivable over the KDS level, even as the database size grows. The tradeoff between accuracy, mentioned typically as a recall and the search latency, mentioned typically as a credit throughput, is important in evaluating ANS performance. Several implementations of large-scale ANS are now powering enterprise-grade, mission-critical and -- scale search applications. In these scenarios, benchmarks such as costs, preprocessing time, and the power consumption becomes just as important as the legal versus latency tradeoff. I have discussed on past calls, the APU's advantage in power consumption from our relationship with our Rad-Hard to crypto miners who run their own print hardware. We all know the importance of acquiring power efficient hardware to lower the power bill. Power is a big problem today on how much we consume to generating the power for our machines. Here are a couple of eye-opening statistics. Global data centers consume an estimated 205-terawatt hours in 2018, or 1% of global electricity use. The amount of energy used by data centers doubles approximately every 4 years, meaning that data centers have the furthest growing carbon footprint of any area within the IT sector. This problem will likely increase as workloads become more data-intensive and AI-centric, new hardware and the chipsets specifically designed for power efficiency will continue to be a major component of the design of future data centers. Solving this problem is one of the strategic opportunities for our technology. GSI can document in numerous applications that our technology brings enormous power saving because our typical system require a much smaller footprint. We lowered overall total cost of ownership. Two of the dealership -- in the building scale approximate -- VL search challenge competition will rank participants relative to power usage and hardware costs. Like the other organizers and panelists, GSI will have a submission to the competition. Of course, we hope to attain a leadership outcome for our submission and garner increased visibility with other competitors, including Nvidia, Microsoft, Intel, and other leading AI hardware vendors. We mentioned on our last call that we are a partner in OpenSearch 1.0 launched by AWS with our Elasticsearch k-NN plugin. We are still in the early stage of developing this opportunity. We have a functioning server in our Silicon Valley data center where we have had an initial customer demo. At this time, the release of the new OpenSearch 2.0 protocol has been delayed, pushing out the timing of our launch. We expect our next round of demos will happen once the next reversion of OpenSearch is released. It's our goal to open our facility to beta customers in calendar 2022. This quarter, we successfully raised the profile of GSI in the industry with our participation in the ANS competition. We are moving forward with opportunity that have followed our recent success in prior competition with the APU and the announcement of the radiation-tolerant NASA. The GSI team has been working tirelessly to raise our profile with the goal of leading data customers and building a pipeline of business. I sincerely thank you for your support as a fellow GSI shareholder. Now I will hand the call over to Didier, who will discuss our business performance further. Please go ahead, Didier.
Didier Lasserre: Thank you, Lee-Lean. As Lee-Lean stated, we have identified the market segments we believe are most relevant to the APU and defined our total available market, or TAM, and also our serviceable available market, or SAM. Starting with the total opportunity for the APU search applications based on the available data, we estimate the global TAM is $137 billion in 2021 and will grow at a CAGR of approximately 20% to $287 billion by 2025. Our SAM is $4.5 billion in 2021, growing to $10 billion by 2025. The multiple search market segments we include in our analysis include computer vision, synthetic aperture radar, drug discovery, cybersecurity and service markets such as NoSQL, Elasticsearch and OpenSearch, which we plan to support with a SaaS solution. These are market segments where we have -- currently have ongoing customer engagements. Computer vision is a broad term that includes image and object recognition, facial and body recognition, warehouse robotics, automatic target recognition and dense registration, which is aligning a photo image with a previous one, which is beneficial for mapping. These are all markets where our chip is highly relevant. Near term, we are engaged with opportunities in facial recognition, object identification, SAR and reidentification. Reidentification is sorting of faces or objects not in the database that allows this unknown object to be recognized and note any patterns related to where and when their image is captured. With regards to the supply chain constraints facing our industry, like many semiconductor companies, we face a 1-year lead time on substrates used in our chip assembly and supply remains tight. There was a fire at one of our substrate suppliers, but they are now back up to 70% capacity, which is helping. In Taiwan, we had a subcontractor that shut down in June for 4 weeks due to a COVID outbreak in their factory. It's now 100% back in place. Regarding the recently announced TSMC wafer price increases, we are looking at 20% increase in wafer costs, with the new prices becoming effective for all new orders. Overall, the impact of increased assembly costs -- I'm sorry, assembly labor cost, rising substrate prices, increased wafer costs and expedite charges means that we are -- we will be increasing our prices to all of our customers. Moving to the sales breakdown for the second quarter of fiscal 2022. Sales to Nokia were $1.9 million or 23.8% of net revenues compared to $3.4 million or 51.7% of net revenues in the same period a year ago, and $3.8 million or 42.7% of net revenues in the prior quarter. Military defense sales were 27.4% of second quarter shipments compared to 26.9% of shipments in the comparable period a year ago and 20.0% of shipments in the prior quarter. SigmaQuad sales were 52.4% of second quarter shipments compared to 65.4% in the second quarter of fiscal '21 and 63.6% in the prior quarter. I'd now like to hand to Doug -- the call over to Doug. Doug, please go ahead.
Douglas Schirle: Thank you, Didier. We reported a net loss of $4.6 million or $0.19 per diluted share, and net revenues of $7.8 million in the second quarter of fiscal 2022 compared to a net loss of $5.2 million or $0.22 per diluted share, and net revenues of $6.7 million for the second quarter of fiscal 2021. And the net loss of $4.2 million or $0.17 per diluted share, and net revenues of $8.8 million for the quarter of fiscal '22. Gross margin was 53.6% compared to 46.7% in the prior year period and 54.4% in the preceding first quarter. The changes in gross margin were primarily due to changes in product mix sold in the three periods. Total operating expenses in the second quarter of fiscal 2022 were $8.7 million compared to $8.3 million in the second quarter of fiscal 2021 and $9.1 million in the prior quarter. Research and development expenses were $5.9 million compared to $5.7 million in the prior year period and $6.1 million in the prior quarter. Selling, general and administrative expenses were $2.8 million in the quarter ended September 30, 2021, compared to $2.6 million in the prior year quarter and $3 million in the previous quarter. Second quarter fiscal 2022 operating loss was $4.5 million compared to $5.2 million in the prior year period and $4.4 million in the prior quarter. Second quarter fiscal 2022 net loss included interest income and other expense net of $8,000 and a tax provision of $42,000 compared to interest income and other expense net of $16,000 and a tax provision of $62,000 for the same period a year ago. In the preceding first quarter, net loss included interest and other expense of $20,000 and a tax benefit of $172,000. Total second quarter pretax stock-based compensation expense was $716,000 compared to $653,000 in the comparable period a year ago and $823,000 in the prior quarter. At September 30, 2021, the company had $50.7 million in cash, cash equivalents and short-term investments and $2.8 million in long-term investments compared to $54 million in cash, cash equivalents and short-term investments and $5.8 million in long-term investments at March 31, 2021. Working capital was $53.6 million as of September 30, 2021, versus $56 million at March 31, 2021, with no debt. Stockholders' equity as of September 30, 2021, was $69.9 million compared to $75.6 million as of the fiscal year ended March 31, 2021. We still see the supply chain constraints having a modest impact on our ability to fill all of our orders, but there has been some improvement. The supply chain situation remains fluid, and we do not expect significantly from these constraints before next year. Given these variables, current expectations for the upcoming third quarter are net revenues in the range of $7.2 million to $8.2 million, with gross margin of approximately 52% to 54%. Operator, at this point, we'll open the call to Q&A.
Operator: . Our first question comes from the line of Rajvindra Gill with Needham & Company.
Denis Pyatchanin: This is actually Denis asking a few questions for Raji. So I'll just start off with a question about the price increases. When do you expect that the price increases that you're passing on to your customers that will start to show up in the top line?
Didier Lasserre: So we are changing backlog and all new orders as of December 1.
Denis Pyatchanin: All new orders, got it. Wonderful. And then the other question that I had was relating to kind of the Gemini-I APU. I think on the -- a few calls ago, you mentioned that the Gemini-I APU should be seeing some volume production in this current quarter. How is that milestone being met? Are you beginning production or the supply chain constraints kind of limiting that? Can you give us an update on the Gemini-I volumes?
Lee-Lean Shu: Yes. From the component level, I think we are in the preproduction mode, which means that we are still doing the qualification. But this is already before we see the demand for it. And I think, right now, the major challenge is still on the software. We have to make a lot of software API ready for the customer. So that -- the software will be major barrier for the hardware availability.
Denis Pyatchanin: So you're saying that once the software is ready, that will kind of get you one step closer to the actual production of the Gemini-I?
Lee-Lean Shu: Yes. From the hardware point of view, yes, we are ready. We just need to win the application with software.
Operator: Our next question comes from the line of Jeff Bernstein with Cowen.
Jeffrey Bernstein: Congratulations on the new patent, you guys were just issued on the SRAM structure optimized for in-memory computing. And from that patent, you can see there's not a large instruction set here and it's going to be important for you guys to provide compiler and software libraries that make the chip easier for people to use. So can you just give us some detail on what the deliverables are there and the timing for those?
Lee-Lean Shu: I think right now, a lot of things we are doing is really for the spreading awareness. We -- I think we do 10 months a year. We have good performance, and -- compared to what the solution already in the industry. But I think we still made a lot of work to develop the market and to get -- to win the confidence of the customers. And so we -- as we have described it before, we have a lot of -- we are participating, the competition. So winning the award. I mean, all this is for the brand awareness. And hopefully, we can demonstrate our capability among our peers and also among the customers. So that's our major focus now.
Didier Lasserre: And Jeff, just to add to that, for the deliverables, obviously, the compiler stack is critical to that because as Lee-Lean mentioned, we're writing a lot of the APIs and the algorithms today, but a lot of folks want to write their own or there's just -- we can't keep up with all the different applications. So the compiler stack is critical. And so we're going to have that release to our beta guys. We already have the beta guys identified. And that will be -- we were hoping to do it by the end of the year. It looks like it might be falling into early next year for the beta guys. And so they'll go through it and they'll play with it, and they'll try to break it and everything else. And then we'll release it to the general public sometime in 2022. I'm hoping it will be sometime first half of 2022 where it will be available for everyone.
Jeffrey Bernstein: Got you. And then -- so then I'm assuming that for certain vertical kinds of applications that are somewhat generalizable that multiple customers might want like the synthetic aperture radar analysis or object recognition, et cetera, that you guys are doing libraries to make that even easier for people. Is that correct? And can you just -- of those vertical markets you touched on earlier, can you just talk about when each of those is going to be delivered?
Didier Lasserre: Correct. So you have both libraries and you also have further up, the stack, the actual APIs or the algorithms. And so as you mentioned that has either been done or is being done for things like the SAAR, the synthetic aperture radar. You know that we've written it also for the BIOVIA pipeline pilot platform. It's already been done. We've done it for facial recognition and object detection. We are in the process of doing it for the dense registration, and we've done some work on reidentification as well. So there has been a lot of work that's been done already.
Jeffrey Bernstein: Got you. So is it fair to say that by the middle of next year, some of these key verticals will basically be covered and very accessible to customers who want to start using them?
Didier Lasserre: If they want to use our algorithms, correct. Yes. And like I said, some of the entities we deal with, getting external software is a problem. And so the answer is, for folks where it's not a problem, those should be available for folks that want to write their own, that's where we're relying on the compiler stack.
Jeffrey Bernstein: Okay. Got you. Okay. And then just I want to understand a little bit more about the environment that you're facing out there in terms of sales and understand that you guys are in the model execution, not training part of the market. But what we see in the training market, AWS just did an Elasticsearch instance based on the Intel Habana chips. So they're kind of open to outsiders. They have their own Trainium custom chips. It looks like Microsoft is got Graphcore, Groq and Nimbix as training chip partners that they're working with, where some of the others like Baidu, Alibaba and Google are only doing their own. Talk about the not invented here? Or who is open to look at outside chips among those big players out there that could be important partners?
Didier Lasserre: Sure. So you bring up a good point. Almost all of the large big data guys have internal chip development happening, and they've always had that. But with that said, certainly, if you bring a solution that's going to accelerate a search, or it's going to lower the power, or it's going to do something that's important to them, they're open to it. I mean we haven't seen anybody that basically said, no, we're designing our own. As you said, a lot of the chips these guys are doing are for very specific functions that are important to them. And a lot of it is centered around the training, as you mentioned. We haven't seen that level of activity for specific search, which is what we're focused on.
Jeffrey Bernstein: Got you. But your feeling is that delivering these kinds of capabilities to some of these large players, they will be open to looking at these and possibly offering instances on our networks?
Didier Lasserre: Correct. I mean -- and we've seen that already play out with the OpenSearch. Again, we've demoed on 1.0, and we're waiting for 2.0, but they've already announced us as a partner on that. So yes, they're open.
Jeffrey Bernstein: Got you. Okay. And then I just wanted to ask on the non-Nokia customers, 48% of revenue, that's the highest since before the pandemic. Any particular customers that are growing outside of military? Or what's sort of the status of non-Nokia customers?
Didier Lasserre: Yes. So we were -- as you looked at the numbers, Nokia was down more quarter-over-quarter than our overall revenue was. So obviously, the rest of our revenue grew outside of that. And so we saw strength in some areas that -- it wasn't one particular area. There was certainly some strength in the military sector. We also had shipped the end of that last order for the Rad-Tolerant that we had mentioned. We shipped half of it in the June quarter and half of it in this past September quarter. And then we also saw a little bit of the rebounding in some of our customers that do automotive equipment, test equipment, things like that. Obviously, the automotive sector had been hit by the availability of chips earlier this year. And so they were a little bit quiet in the first half and now that seems to loosen up. So some of the equipment guys have come back to us this past quarter as well.
Jeffrey Bernstein: Got you. That's great. And then just a request. Jim Ramel, the largest shareholders asked you guys to get an IP valuation, which makes sense to me. I think we're now your second biggest nonindex holder. And just kind of back of the envelope, it looks like you're building that you own at Elko Drive, is worth over $10 million potentially. And I think it would be worthwhile to have that appraised and have shareholders understand that there is some additional asset here that's significant beyond the cash that you guys have.
Douglas Schirle: That's true. We believe that the buildings is obviously worth a lot more than what's on the books. We've been in it for over 10 years now, of course.
Jeffrey Bernstein: Okay. Well, it would be great to have appraisal of that.
Operator: Our next question comes from the line of Brett Reiss with Janney Montgomery Scott.
Brett Reiss: Have any crypto miners, in fact, approached you in possibly using the Gemini system?
Lee-Lean Shu: We have a look at the crypto mining. I think, right now, in the market, there are dedicated chip developed for them. So it's -- APU is not design specific for that. So we are -- so even though we look at that, we don't believe we are suitable, okay? But from the general -- if somebody wants a general purpose -- possibly. Hardly usable, but not in the form of doing the crypto mining as a revenue and all that.
Brett Reiss: Right. Right. Now for the ongoing customer engagements to vest into material revenues for the company, is the additional engineering that must be done in the control of our company? Or is it additional engineering that must be done by the potential customers engineering teams to make the Gemini system a commercially viable product for us?
Didier Lasserre: So it depends, right? The answer is yes to both, right? I mean, we certainly need to continue to develop more algorithms, more libraries, more APIs. But then we also need to make that compiler stack available so that they can do it themselves. And -- because, again, there are way too many potential applications going forward for us to keep up and there are some people who just don't want to outsource that. So they'll do it themselves. And supplying them, that compiler stack, will allow them to not have to basically program at the registered level. I mean, they'll be able to do it on a much higher language. And so the answer is, yes, we will need to do some of it and that will be done internally, and then we need to enable our customers to do their own as well.
Operator: Our next question comes from the line of George Gaspar , a Private Investor.
Unidentified Analyst: Just ongoing with the last question. Can you give us a very specific example of the testing on a specific entry into the market that you're running against competition. Can you give us something that would identify with your capacity being much superior to the others out there?
Didier Lasserre: Sure. So I can give you one right off the top of the bat. Sorry, dropped my glasses. So we are engaged with the customer now. We are about ready to do a POC -- I'm sorry, a proof of concept, specifically for the SAAR, which is the synthetic aperture radar. And how we got to where we are today with them is we did benchmarking for them. And it was a benchmark that was based off of a 5 kilometer by 5 kilometer image, and they needed resolution down to 0.5 meter in 1 second -- or 1 millisecond, sorry. And so we did the benchmarking on CPUs. We did them on GPUs, and we did them on our APU, the Gemini, and we showed them all of the test data and the benchmarking. And that -- and we ended up being chosen. And so we're going through the process now of doing the POC definition with them. And so that's -- our application is a perfect example of they actually look at the data from the three possible chip technologies and chose GSI.
Unidentified Analyst: Okay. Interesting. And just ongoing on that, getting back over on the defense -- the military side. There seems to be a continuing requirement for space work to identify and interpret higher faster speeds. And is there any possibility of you expanding your activity on the defense military side because of the speed that your -- the capacity that you have relative to others?
Didier Lasserre: Yes, absolutely. Several of these applications we've talked about, SAAR, object detection, ATR, which is the automatic target recognition, all those are kind of a military application. And there are certainly other ones we've worked on, signal classification and the such. So there are certainly several sectors and applications we've worked on for that market. And then as you know, we were one of the -- we partnered with Space Micro and won a NASA SBIR, and that SBIR was around what NASA calls an IPU, which is inference processing unit, but it's essentially a ruggedized board using our solution that can be used in space. And so as we discussed, it was -- some time ago, it was almost coming up on 2 years ago, we did some initial SEL testing on the APU and it came back very, very good. And we are going to, some time, and hopefully, the first half of 2022, follow on that testing and do SEU and SEFI kind of testing to round out everything that's required to be able to put the Gemini in the space. So yes, we're certainly going to continue to pursue that avenue as well.
Unidentified Analyst: That's very interesting. I got one question on the issuance of stock in the last quarter. How many shares have been issued? And was that for issuance to employees basically, for the increase in shares? Can you elaborate on this aspect?
Douglas Schirle: I don't have the actual number here in front of me, George, but it's somewhere between 100,000 and 200,000 shares.
Unidentified Analyst: Between 100,000 to 200,000 shares?
Douglas Schirle: That must be somewhere about 100,000 to 200,000 shares.
Unidentified Analyst: I see. So those...
Douglas Schirle: And then since the beginning of the year, fiscal year, you have some ESPP purchases and option exercises. In fact, just this past week or so, we had two Directors exercised some options. And I believe there are Form 4s out there for each of those.
Unidentified Analyst: Okay. All right. And at this point, being that the stock has been lower for some time here, is there -- has there been any purchasing of -- in the marketplace to recover shares that are outstanding?
Douglas Schirle: We haven't done any repurchases for over a year now, if that's what you're asking.
Unidentified Analyst: Yes, right. All right.
Operator: . Our next question comes from the line of John Fichthorn with Dialectic Capital.
John Fichthorn: I guess as a quick follow-up to the last question, I've got to ask with $50 million of cash and a $10 million building and a bright outlook maybe why you wouldn't be doing share buybacks right now since it was asked?
Douglas Schirle: Well, we've had a long history of buying back shares. We went public in March of 2007 and netted $30 million in the offering. And we've already repurchased over $61 million worth of our stock. So we've already taken, in terms of dollars, 2x off the market based on what the IPO was. And we have cash in the bank, but we understand that the APU is a very exciting product. There is a very large market that we're addressing with that. But there's still work to do. And we just feel that -- we want to make sure that we're going to be successful. And yes, the stock is cheap. We all believe that. But we think that, at this point, given we've already done in terms of stock repurchases, it makes sense to keep the cash at this point and make sure that we're successful with our development efforts.
John Fichthorn: Well, that makes sense, and it leads to my second question, which is really about your go-to-market. You've talked about a proof of concept, you're going in. You've talked about competitions that you're entering in. You've talked about a number of your potential customers or maybe even acquirers who knows that are also in-house competitors. And so I guess I don't fully understand although it now sounds like it's pushed off until kind of Q1, Q2 of next year, what the go-to-market strategy is at this point? And so I don't -- maybe you don't know either, but to the extent that maybe I just missed it, I'd love you to kind of give me as much granularity as you're willing to on that.
Didier Lasserre: I mean -- so certainly, obviously, we have several possible applications. I mean, the overall umbrella is search. I mean, that's just a generic term, right? But under the search falls all these different applications. And so obviously, the go-to-market is where can we show benefit in these markets, right? Who sees the most value in our solutions? So that's where we've been focusing. And also, where is the TAMs as well. That's why we've gone through this TAM analysis, which we've discussed. We've broken it up by segments. And so right now, it's going through that process of showing these -- the customers in these segments what our value is. In some cases, it's lower power. In some cases, it's faster -- more queries or faster response times, more accurate responses. Sometimes it's lower cost. One of the other benefits that we're able to show customers, and this one is really at the beginning stage, and it's taking some conversations to open up their eyes, but we can also offer, in a lot of cases, a mobility aspect to their solution, where in the past for them to do a certain function would require cabinets of CPU-based servers or cabinets of GPU, while with our solution, it could be a couple of racks, which can now be put in a plane, in a van, in a submarine or what have you. And so there's -- that's one of the things that to be able to -- for them to understand that possibility that all suddenly they can have the solution be mobile as well.
John Fichthorn: Maybe I should drill down a little bit though on -- I get it. You've got a lot of exciting applications. There's a big TAM. But either when or in what application do you think you're furthest along and might get the first signs of revenue? When should we think there's going to be somebody who signs a piece of paper that says, I want to pay you for this? And then what application do you think that might be? And -- or what couple of applications are you furthest down this chain and not just in a proof of -- maybe proof of concept is the furthest. But where are you the -- for this today?
Didier Lasserre: I would say it's in the government and military sectors. So areas like the SAAR application, some of the object detections that would probably be where we're furthest down the line. I mean, obviously, you know that we're going to be working this OpenSearch 2.0, and that's going to kick in sometime in 2022, but it will be instances. So it will take a little longer for that revenue. It's a service. It's a SaaS model. So that will take a little longer to kick in. Some of it will kick in, in 2022. The question is how much is not, and we don't know yet, obviously.
John Fichthorn: But before the OpenSearch 2.0, will you have revenue from one of these defense-type customers this year, even if it's just NRE?
Didier Lasserre: Possibly. I mean, the -- timing is hard to predict with the military guys. The answer is we were -- we should have gotten a board or 2 purchased about now, and it may happen by the end of the year. It may fall into early next year. The timing is not always easy to predict with these folks. But we're imminent on at least a couple of boards. But again, let's be clear, it's not -- we're not talking volume here. This is still under the kind of a POC where they'll buy a board, they'll test it out, prove to themselves that this is really what it is, what they want and gives them the results that they want and need, and then we'll go from there. So it will be onesie, twosie kind of things...
John Fichthorn: And so in terms of being like production revenue, like real recurring either SaaS revenue or something else, is that really 2023?
Didier Lasserre: Certainly not earlier than second half of next year. I mean, yes, I don't see any way second half of next year would be the earliest. But yes, it could fall into 2023. Again, hard to really predict exactly when that's going to happen.
John Fichthorn: Totally understand, but you know when the soonest it could be, right, if everything goes well. So fine, second half of next year. And so the last time I'll just try and once again specify like in the Defense Department business, I get it because you're not competing against guys inside the defense department making the same product. But in a lot of these other instances, in the commercial market, you are. And so just give me a quick like is the -- is it just to win competitions and hope people recognize you because you're still going to have guys inside of Google and Microsoft and Baidu and everywhere else going no, use ours, you pay us anyway. And we've designed it for our own ship? Or is there like you guys have feet on the street, knocking on doors? Like just help me understand what the commercial go-to-market strategy is specifically?
Didier Lasserre: No, we're, of course, talking to these folks. And again, as I mentioned earlier, they're all doing chip designs, like you said. And like -- I can't remember if it was Jeff who had mentioned that. But I -- we haven't seen where it's really specific on the search per se. It's generally some kind of function. But the guys we're talking to, again, the -- OpenSearch is part of AWS. And as you say, AWS is doing -- or Amazon is doing their own chips. So why is it they've already chosen us as a partner? Because obviously, they're not doing anything internal that matches what we're doing. And so we will always be competing against some internal design. But -- and when I say we, chip manufacturers. But we haven't seen where the -- what we're offering our kind of value. We haven't seen that effort within these companies in the search area.
Lee-Lean Shu: The large-scale database search is still a very new market. AWS OpenSearch, they just started just a couple of months ago. And we have this competition bidding scale -- and search. I mean, that's the first of its kind. The industry never have this competition -- this kind of competition before. So it's just getting started. And Gemini APU is, by far, we have seen the best hardware only that do this kind of job. We hope we can perform well in the competition. So that will demonstrate our valuations in this area.
John Fichthorn: I mean, I guess my concern is that your point exactly, that this is very new, very new, right? You're about to get to 2.0, and we're talking about revenue that might start to ramp in late '23. And so I just -- I don't know what's your backup plan if we're sitting here having this call at the end of '23, and you're like, well, it should be in second half '24. It's very new, right? People are still doing evaluations or at the end of '24, and it's '25. Like at what point do you have another path? Or is there no path? This is the one path and we're all in?
Lee-Lean Shu: No, no. We are more than that. We are -- well, the search is the one area we are looking at, right? The other thing is that we talk about is the defense and the government project. And the NASA project, I mean, that is to develop the device, which can go into the space. So with this development, the prime contractor can pick up this device and then put into a satellite, and it can apply to many, many companies and many, many applications. So it's more than the search. I mean, we talk about we have object recognition. We can do SAAR. We can do sensor registration. Many, many computer vision type of things. So we are hopeful. And right now, one thing is that we are not certain is when the production revenue will come in, but we hope it's coming soon, and we believe so.
Operator: At this time, we have reached the end of the question-and-answer session.
Lee-Lean Shu: Thank you for all -- thank you all for joining us. We look forward to speaking with you again when we report our third quarter fiscal 2022 results. Thank you.
Operator: This concludes today's conference. You may disconnect your lines at this time. Thank you, and have a great day.
