Ladies and gentlemen, thank you for standing by. Welcome to GSI Technology's First Quarter Fiscal 2024 Results Conference Call. At this time, all participants are in listen-only mode. Later, we will conduct a question-and-answer session. At that time, we will provide instruction for those interested in entering the queue for the Q&A. Before we begin today's calls, 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 involves risks and uncertainties that could cause actual results to differ materially from those anticipated. These risks and uncertainties are described in the company 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, July 27, 2023 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 like now to turn the conference over to Mr. Shu. Please go ahead, sir.

Good day, everyone. And welcome to our first quarter fiscal year 2024 earnings call. We are happy to update you on our achievement of milestones on our journey to innovation and growth. Our dedication and focus have allowed us to make good progress during our first quarter of fiscal 2024. Let's start with our progress on the fencing our growth in innovation objective. With our commitment to lend Gemini-I customer, we have moved forward to the demo with two of our soft targets. Additionally, we added new resources to address the fast vector search market at home with our product for this application. Didier will provide more color on this in his comments. Additionally, I'm pleased to share that Version 2 of our L-Python compiler stack is on track for release to beta customers by the end of this summer. This marks a significant step following our product roadmap, enabling us to deliver cutting-edge solutions into our customer satisfaction. Your pattern is designed to make it easy for other developers to contribute and improve the software. The appeal of L-Python is that it can be used on different operating systems like Windows, Linux, and Mac OS. The reason L-Python is so fast is that it performs optimization at both the high level and the low level. This means it tries to make the code more efficient before running it. Additionally, L-Python allows for easy customization of the different ways you can convert the code, which can be useful for specific days or preferences. Not only is L-Python fast and flexible, but the stack is also usable for other applications. We believe we could readily create the ecosystem beyond the APU. We are closing in on successfully completing the table of Gemini-II, which is to be finalized and sent off to TSMC in the next few weeks. This effort is a major achievement and showcases our commitment to push the boundaries of AI chip technology. Our tool is extremely beneficial, and the successful completion of this master serves as a testimony to our talented team's hard work and equity. We anticipate entering the solution during the second half of calendar year 2024. We remain focused on driving innovation, delivering exceptional products and leveraging those trends to foster strategic partnerships that will help propel our company forward. The strategic addition to our team reinforces our commitment to drive growth faster in partnerships and deliver innovative solutions to our customers. We are excited about our opportunities and the value individuals will bring to our organization as they work with our dedicated team to position us for success. I want to extend my gratitude to our employees, customers, and shareholders for their unwavering support. Together, we will continue to build a brighter future for our company. Now, I will hand the call over to Didier who will discuss operating development and sales activities. Please go ahead, Didier.

Thank you, Lee-Lean. I want to start by addressing a point mentioned earlier by Lee-Lean; we have strengthened our team with the addition of two highly skilled professionals who will play pivotal roles in developing strategic partnerships with hyperscalers and establishing our presence in the fast vector search market. These individuals bring a wealth of knowledge and extensive experience in their respective fields. One of our new team members, who will assume the senior data scientist role, will lead our team on various projects and offload some of the workload from our division in Israel. With this team, we will transfer some functions to the U.S., including developing software applications, functions, and undertaking government-related projects that require collaboration with U.S.-based employees. Our U.S. data science team will play a crucial role in assisting customers with the compiler and conducting benchmarks across different platforms. Our new data scientists will collaborate with this team to optimize our plugin for fast vector search, paving the way for the successful deployment of this business line for our company. Our second new resource brings a wealth of experience from the semiconductor sector, having worked for leading FPGA companies. This background has afforded him extensive industry connections which will be invaluable as we strive to engage and form partnerships with our top hyperscalers. He will lead the building of our platform to explore strategic partners for our APU technology to develop service and licensing revenue resources to fund future APU development. On the last call, we mentioned we were working with a major hyperscaler based on Gemini architecture for inference of large language models. This relationship holds great potential for our growth. We have conducted a feasibility study exploring Gemini architecture. I am delighted to say that we are making great progress in this prospect. The study specifically focuses on GPT inference utilizing a future APU. We found that the APU, when compared to existing technologies, can achieve significantly enhanced performance levels while utilizing the same process technology. GPT is a memory-intensive application; it requires a very large and very fast memory hierarchy from external storage memory all the way to the internal processors' working memory. For the GPT 175 billion model, 175 gigabytes of fast memory is required to store the model's parameters. This can be accomplished by incorporating a processor die and several HBM, which are high bandwidth memories, and will be put on a 2.5D substrate. It also requires large internal memory and very fast internal memory next to the processor core as a working memory to support the large matrix multiplication performed by the processor core. The APU architecture inherently has large built-in memory and large memory bandwidth that not only provides memory throughput but also supports very high performance computation. Gemini can achieve similar peak tops per watt as state-of-the-art GPUs on the same process technology node. However, with our massive L1 size and large bandwidth, the APU can sustain average tops nearly the same as peak tops, unlike a GPU. In a single module composed of a 5-nanometer Gemini dye plus six HBM three dies, we have calculated that we could achieve more than 0.6 tokens per second per watt, with the input size of 32 tokens to generate a context of 64 tokens in the GPT 175 billion model. This output is more than 60 times the performance that can be delivered by a state-of-the-art GPU at a slightly better technology node. The study was done in conjunction with laying out the development roadmap for Gemini III to move further into generative AI territory. The APU holds a distinctive advantage in delivering low power consumption at peak performance levels, given the in-memory processing capability. As we have seen, generative AI applications like ChatGPT are becoming more capable with each generation. The driving force behind this improvement capability is the number of parameters used by the large language models that power them. More parameters require more computation, leading to higher energy usage and a much larger carbon footprint. To help combat the growth of the carbon footprint, researchers are exploring new ways to compress data to reduce memory requirements. These are trade-offs between the formats that researchers are investigating. To navigate these trade-offs, they need a flexible solution. Unfortunately, GPUs and CPUs lack this flexibility and are limited to a small fixed set of data formats. GSI Technology's APU technology provides the flexibility to explore new methods. By allowing computation to be performed at the bit level, computation can be performed on any size data element with a resolution as fine as a single bit. This will allow innovative solutions to be developed and will reduce energy by optimizing the number of usable bits for each data transfer. As we work with potential strategic licensing partners, we can increase the awareness of our capabilities to solve some of AI's biggest challenges. Regarding our work on Gemini I solution, we have made notable progress with two of our SAR targets. Underscoring our commitment to expanding our presence in this market, we have set a goal of closing a sale in FY 2024 with one of these customers. As I mentioned, we recently added resources to support our beta fast vector search customers. With additional resources in place, we anticipate building a SaaS revenue source with customized solutions for fast vector search customers before the end of the fiscal year. Let me switch now to the customer and product breakdown for the first quarter. In the first quarter of fiscal 2024, sales to new Kia were $1.9 million or 33% of net revenues compared to $1.3 million or 14% of net revenues in the same period a year ago, and $1.2 million or 21.8% of net revenues in the prior quarter. Military defense sales were 33.8% of first-quarter shipments compared to 22.3% of shipments in the comparable period a year ago and 44.2% of shipments in the prior quarter. SigmaQuad sales were 58.6% of first-quarter shipments compared to 44.8% in the first quarter of fiscal 2023 and 46.3% in the prior quarter. I would now like to hand the call over to Doug. Please go ahead.

Thank you, Didier. GSI reported a net loss of $5.1 million or $0.21 per diluted share on net revenues of $5.6 million for the first quarter of fiscal 2024 compared to a net loss of $4 million or $0.16 per diluted share on net revenues of $8.9 million for the first quarter of fiscal 2023 and a net loss of $4 million or $0.16 per diluted share on net revenues of $5.4 million for the fourth quarter of fiscal 2023. Gross margin was 54.9% in the first quarter of fiscal 2024 compared to 60.2% in the prior year period and 55.9% in the preceding fourth quarter. The year-on-year decrease in gross margin in the first quarter of fiscal 2024 was primarily due to the impact of fixed manufacturing costs and our cost of goods on lower net revenue. Total operating expenses in the first quarter of fiscal 2024 were $8.2 million compared to $9.3 million in the first quarter of fiscal 2023 and $6.9 million in the prior quarter. Research and Development expenses were $5.2 million compared to $6.6 million in the prior year period and $5 million in the prior quarter. Selling, general and administrative expenses were $3 million in the quarter ended June 30, 2023 compared to $2.7 million in the prior year quarter and $1.9 million in the previous quarter. We estimate that through June 30, 2023, we have incurred research and development spending in excess of $140 million on our APU product offering. In the first quarter of fiscal 2024, operating loss was $5.1 million compared to an operating loss of $3.9 million in the prior year period and an operating loss of $3.9 million in the prior quarter. First quarter fiscal 2024 net loss included interest and other income of $80,000 and a tax provision of $51,000 compared to $26,000 in interest and other expense and a tax provision of $60,000 for the same period a year ago. In the preceding fourth quarter, net loss included interest and other income of $101,000 and a tax provision of $191,000. Total first quarter pretax stock-based compensation expense was $820,000 compared to $638,000 in the comparable quarter a year ago and $515,000 in the prior quarter. At June 30, 2023, the company had $27.7 million in cash, cash equivalents, and short-term investments compared to $30.6 million in cash, cash equivalents, and short-term investments at March 31, 2023. Working capital is $32.1 million as of June 30, 2023 compared to $34.7 million at March 31, 2023 with no debt. Stockholders' equity as of June 30, 2023, was $48.6 million compared to $51.4 million as of the fiscal year ended March 31, 2023. During the June quarter, the company filed a registration statement on Form S-3 so that the company would be in a position to quickly access the markets and raise capital if the opportunity arises.

At this point, we'll open up for Q&A.

Nick Doyle from Needham. Thanks for taking my questions. Just first, could you expand on the drivers behind gross margin this quarter and next quarter? So, we could see a little bit of a decline this quarter and you're expected to increase next quarter? Could you just expand on why that's happening?

Yes, it's really related to product mix. We do our best effort at forecasting what we believe that revenues are going to be during the quarter. But obviously with only about a third or so of the quarter booked at the beginning of the quarter, we have to estimate where the revenues are going to come from. It is strictly tied to product mix, nothing more.

Okay. Could you just tell us what part of the mix was higher this quarter that’s driving the lower margin?

Yes, the biggest thing that impacts the margin is that we have quite a bit of military business and that has the highest margin. Nokia revenues are generally at a reasonable level and that also was a good margin. So it really is dependent on probably the biggest factor - military sales at this point.

Okay. Great. Makes sense. So you talked about how you tested your APU, which can basically sustain higher tops and drive better performance per watt with this specific GPT application. Can you just expand on how that's done and how your APU differentiates from CPUs and GPUs on the market? Is it entirely to do with the ability to do computations at the bit level? That was my understanding; yes, any detail there will be great?

Yes, first of all, key few has a very, very small cache. And I think it is good for graphic processing. But when you talk about the future, future parameters of larger language models, they can only do a fraction of what they can do, from the tops point of view. In the GPU, we have a huge memory inside the chip. And we calculated the tops, particularly from how we can support the processing with our memory. That’s how we come up with the tops. So that's why we are able to sustain both average and peak tops. So I hope I answered your question?

Okay. I know I can sneak one more. I think in the past, you talked about the cost of Gemini II being about $2.5 million. Is that still the case? And is that entire tape out costs behind us or is it still ongoing?

I hope I answered your question. I know I can sneak one more. I think in the past, you talked about the cost of Gemini II being about $2.5 million. Is that still the case? And is that entire tape out costs behind us or is it still ongoing?

Yes, the $2.5 million is a tape out cost. So, we will have a tape out; the expense could hit later this quarter or early part of the October quarter. But yes, that's just the tape out cost. We've incurred, as we said in our comments, probably in excess of about $140 million developing this product line. And that's what you want in G-II.

Great. Thanks.

Just one comment, we published a white paper on our website. We have a further discussion on why APU is good for larger language models. So, if you are interested, look at www.gsitechnology.com.

Our next question comes from Luke Bohn, Private Investor. Please go ahead.

Thanks. So, in terms of the study, could you mention that it was projecting a 5-nanometer architecture for the study about comparing with GPUs and peak performance? I suppose based on your understanding of the engineering and the physics of your APU architecture that you project that is feasible, and is that the case? Can you project even further to say that there is a limit lower in terms of reducing to get even denser architecture?

Yes, we peak phenomena because at this moment, the sale of our processor is either 5 or 4-nanometer. So, we want to have an apple-to-apple comparison. So we pick 5-nanometer as a study base. Of course, if we want to implement a real chip, I think we want to do it with even more advanced technology, so adjusting with everybody else.

Okay. Yes, so that is the tentative plan is to make the lead basically from your current, I think you said 16. Gemini II all the way to the 5 for Gemini III?

Yes. No, no, I'm sorry. Gemini III is to be determined. We picked 5-nanometer just because everybody else is on the 5-nanometer. So it’s a fair comparison.

Right. So just for comparison for the study, because that’s what, as Lee-Lean just said, that’s what the GPUs are on as 5-nanometer. So we wanted to do a straight comparison on technology. That does not mean Gemini-III wouldn't be on that technology; it could be something more aggressive.

Okay, yes. It's not a limit point.

Correct.

Excellent. And in terms of you all having larger memory cache and all the other advantages of flexibility in the memory that I read about in the white paper. How does that apply to comparing the APU to GPUs in machine vision for the fact like real-world vision, talking about EVs, autonomous vehicles and referencing the Tesla earnings call saying that they're buying as many NVIDIA GPUs as they can get their hands on. Are you seeing still similar advantages?

Yes, so I mean, the answer is yes. Our Gemini I we understood was not a fit for what you talked about ADAS. Gemini II, we anticipate to be a better fit, just because of the lack of an FPGA on the board. But the fundamental unique architecture is going to be the same, which is the fact that we are doing the computation or the search on the memory bit line in place. We're not going off-chip to fetch the data, and then going back and rewriting the data. So that's the fundamental unique architecture that we have is in regardless of the market, and is available or there with Gemini I and Gemini II.

Awesome. Yes, this one's just getting that clarification about because you have even talked about the performance being kind of favored for GPUs being apt for visual processing. So, I wonder if I get that clarification about the broader kind of machine vision and visual processing markets there. Yes, that's great. I think I have one more question. Yes, definitely applaud you all for moving forward with the SaaS and vector search, because there have been so many announcements recently about the value of large vector search NLP, neural networks broadly, and seeing how much of that TAM you all can address? Yes, definitely get there the shade you're putting just more traction to that pathway. And I want just kind of funny curiosity, I've noticed the name Gemini associated with accelerated computing, most recently, most prominently with Google. And it always made sense to me in terms of parallel processing. You have the name Gemini and historical reference. But wondering, yes, spin Q and Google, have not also adopted Gemini and wondering if that is at all an encroachment on your intellectual or your trademark? Or if you find that to just be a kind of a humorous affirmation since you're the first Gemini?

No, we definitely looked into it. And the issue we have is that our trademark is for hardware devices, semiconductor devices, and Google is software-related. So there's no overlapping.

Okay, that makes sense. Okay, so is there anything shifted? I'm not sure if you've actually crunched numbers, but in terms of you have your TAM and you have SAM and these new focuses on the large language models? Yes, how do you see kind of the concrete bigger, concrete addressable market projections updated at this point in terms of timeline and size?

Yes, so we're still working on those TAMs and you know, and there's different segments, right? You have the retrieval and you have the generative, and so those are two different areas. We can certainly address the retrieval now with Gemini I and Gemini II; we certainly feel for the generative side, it's going to be more of a Gemini III. But yes, we're working on those TAMs, SAMs now. They're just not available yet.

Yes, yes. I know it's a hard thing to value which is reflected in yes all over the analyst side of things. Yes, that's, I think that's all I've got. Thank you.

Thank you, Luke.

Our next question comes from Jeff Bernstein, TD Cowen. Please sir, go ahead.

Yes, hi, guys. A couple of questions for you. One just on that last answer. You were talking about Gemini I and Gemini II, addressing retrieval. So you mean queries there? And when you say addressing generative, are you talking about training or just clarify that a little bit?

The response, right. So yes, so you're retrieving the data. And that's something we do very well now, but it's really generating the response. And so that requires very, very high memory bandwidth, which we have in very, very high memory cache in general. I mean that's why we talked about pairing up with HBM III for that. And that’s, and that's more on the generative side.

Okay, so training?

No, no inference.

It's to inference, yes. It's not training.

Okay, still inference. Okay. And then as long as you were talking about the potential for a 5-nanometer or more aggressive kind of Gemini III line, what is the current tape out cost? I know that you're not a processor, more like a memory. So it might be less expensive. But what do you think a tape out cost of 5-nanometer would be now?

5-nanometer the mass costs itself about $15 million, and we probably need to have $100 million for the design. So what we are doing right now is we are looking for partners. We are not planning to do it ourselves.

Yes. Okay. And then I just want to talk about the capital situation. You've now got a registration statement in place. Unfortunately, you missed the big run-up in the stock. Why wouldn't you preferentially sell and lease back the headquarters for funds and then have some more tangible progress to show before we started talking about raising equity?

Well, we have looked into the sale of the building, and we haven't decided to do that yet. But that still is an option. Property values are certainly higher than when we purchased the building many years ago. It is an opportunity that we have considered and discussed with the Board, but no decision has yet been made to sell the building.

Got you. Okay. And then just on the Nokia business that if I remember correctly, you guys were in. Now at this point they are pretty old Nokia 7970 and 7950 routers. I don't even see any reference anymore to the 7950. What's going on there? How much lead time would you get if they were licensing that? Would there be some kind of lucrative revenue that you might get out of that? Just give us a little feeling for your understanding of where you are with the Nokia business?

Sure, yes. So as you said as CEO in the 7750 and 7950 platforms there, and they have extremely long life cycles, as we’ve been seeing; we get a 12-month rolling forecast from Nokia. So far, and that's as far as they go, and the 12 months still looks healthy. What they did do a while back is they did what's called a midlife kicker, to try and give a little bit more performance to those existing systems. What that meant for us is that it went from a 72 megabit density to a 144 megabit density part for that midlife kicker. The ASPs are obviously higher on the larger density part. So what we saw is, even though some of the volumes have come down over time, it's been fairly flat on the revenue side just because the increase in the ASPs offset the decrease in quantity. So at this point, it's still going; we still have the 12-month forecast that looks healthy, and that's as much visibility as we get.

Got you. And then obviously, there is movement around the chip shortages and packaging shortages and that kind of thing. Are we now to a more normalized rate here going forward?

The lead times have become more normalized; the pricing or the costs have not. The price increases that that were subjected to us, which in turn, forced us to raise prices to our customers, they're still there. We've kept our ASPs up, and we'll keep them there until there's any kind of movement from TSMC or any of the substrate folks that raise their prices. But at this point, the real change is the lead time; lead times have come down to a more normalized area.

Got you. But just in terms of inventories, we should be at a more normal kind of inventory situation going forward here?

Yes, that's what we fully believe in. Our inventories have dropped the last quarter too, and we expect them to drop the next couple of quarters or so.

Right. Thank you.

One moment, please, while we poll for questions. Our next question comes from George Gaspar, Private Investor. Please sir, go ahead.

Thank you. It's George Gaspar. Just again, I'd like to do on the financing situation based on your current cash position and looking at your current development progress profile. What do you see is your forward view and the need to exercise financing requirement?

Well, at this point, given the materials we've discussed with the Board, this fiscal year will certainly burn some cash, maybe $12 million to $13 million if the revenue numbers hold up. If the revenue numbers hold up next year, we could start turning the corner and actually having more cash at the end of fiscal 2025 than at the end of fiscal '24.

I see. So what you're saying is that based on the way you're moving along, at your present cash position is sufficient for what you're talking about, what your targets are and the development that you see over the next year?

Currently, that's true. That's the situation.

That is okay. All right. Thank you.

Thank you. There is no further question at this time. I'd like now to turn the floor back over to Mr. Shu for closing comments. Please, sir, go ahead.

Thank you all for joining us. We look forward to speaking with you again when we report our second quarter fiscal 2024. Thank you.

This concludes today's teleconference. You may disconnect your lines at this time. Thank you for your participation.