GSI Technology Q1 2024 Earnings Call Transcript

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July 27, 2023

There are 7 speakers on the call.

Operator

Ladies and gentlemen, thank you for standing by. Welcome to GSI Technology First Quarter Fiscal 2024 Results Conference Call. At that time, we will provide instructions for those interested in entering the queue for the Q and A. Before we begin today's call, the company has requested I read the following Safe Harbor statements. The matters discussed in this conference call may include forward look 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.

Operator

These risks and uncertainties are described in the company Form 10 ks filed with the Securities and Exchange Commission. Additionally, I have also been asked to advise you that this conference call is recorded today, July 27, 2023, at the request of GSI Technology. Hosting the call today is Li Anshu, the company's Chairman, President and Chief Executive Officer. With him are Douglas, Shirley, Chief Financial Officer and Didier Lasser, Vice President of Sales. I would like now to turn the conference over to Mr.

Operator

Xu. Please go ahead, sir.

Speaker 1

Good day, everyone, and welcome to our Q1 fiscal year 2024 earnings call. We are happy to update you on our achievement of milestone on our journey to innovation and growth. Our dedication and focus have allowed us to make good progress during our Q1 of fiscal 2020 Let's start with our progress on advancing our growth and innovation objectives. In line with our commitment to land GEMINA 1 customer, we have moved forward to the demo with 2 of to our target. Additionally, we add new resource to address the fast data search market and hone our product for this application.

Speaker 1

Idia will provide more color on this in his comments. Additionally, I'm pleased to share that version 2 of our AI Pacem competitor stack is on track for release to beta customers by the end of this summer. This marks a significant step 4 in our product roadmap, enabling us to deliver cutting edge solutions and drive customer ELOPISSON is designed to make it easy for other developers to contribute and improve the software. The appeal of El Python is that it can be used on different operating systems, not Windows, Linux and Mac OS. The reason your pipeline is so fast is because it performs optimization at both high level and low level.

Speaker 1

This means we try to make the code more efficient before running it. Additionally, El Python allows for easy Not only is the OpEx so fast and flexible, but the stack is also usable for other applications that we believe could readily create an ecosystem beyond the APU. We are closing in on successfully completing the trip off of Gemini 2, which is expected to be finalized and send off to TSMC in the next few weeks. This KVAR is a major achievement and showcase our commitment to push the boundaries of AI chip technology. EMMI tool is extremely contract shift and the successful completion of this milestone serves as a testament to Our talented team's hard work and expertise, we anticipate centering the solution during the second half of calendar year twenty twenty four.

Speaker 1

We remain focused on driving innovation, delivering exceptional products and leveraging those trends to faster strategic partnership that will help propel our company forward. The strategic addition to our team to enforce our commitment to drive growth, fostering partnership and delivering innovative solution to our customers. We are excited about the opportunities and the value these individuals will bring to our organization, because they work with our dedicated team to position us for success. I want to thank our employees, customers and shareholders for their unwavering support and commitment. Together, we will continue to build a bright future for our company.

Speaker 1

Now I will hand the call over to Didier, who will discuss operating development and sales activities. Chris, go ahead, Didier.

Speaker 2

Thank you, Lilleen. I want to start by addressing a point mentioned earlier by Lilleen. We have strengthened our team with the addition of 2 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.

Speaker 2

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.

Speaker 2

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 plug in 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 the 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. We will lead the building of our platform to I'm sorry, 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.

Speaker 2

On the last call, we mentioned we were working with a major hyperscaler based on Gemini for inference of large language models. This relationship holds great potential for our growth and we recently added additional resources to this team. We have conducted a feasibility study exploring GEMINI architecture and 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.

Speaker 2

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. In the GPT 175,000,000,000 model, 175 gigabyte of fast memory is required to store the model's parameters. This can be accomplished by incorporating a processor die and several HBMs, which are high bandwidth memories. And they'll be put on a 2.5D substrate.

Speaker 2

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. APU architecture has inherently 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 die plus 6 HBM3 die, we calculated that we could achieve more than 0.6 token per second per watt with the input size of 32 tokens to generate a context of 64 tokens in GPT 175,000,000,000 model.

Speaker 2

This output is more than 60 times the performance that could be delivered by a state of the art GPU and a slightly better technology node. This study was done in conjunction with laying out the development roadmap for Gemini 3 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 Chat GPT are becoming more capable with each generation. The driving force behind this improvement capability is the number of parameters used by the large language model that power them.

Speaker 2

More parameters require more computation, leading to higher energy usage and a much larger carbon footprint. To help combat the carbon footprint growth, 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.

Speaker 2

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 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 1 solution, we have made notable progress with 2 of our SAAR targets, underscoring our commitment to expanding our presence in this market.

Speaker 2

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 solution for fast vector search customers before the end of this fiscal year. Let me switch now to the customer and product breakdown for the Q1. In the Q1 of fiscal 2024, sales to Nokia were $1,900,000 or 33 percent of net revenues compared to 1,300,000 or 14% of net revenues in the same period a year ago and 1.2% or 21.8% of net revenues in the prior quarter.

Speaker 2

Military defense sales were 33.8 percent of 1st 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 percent of 1st quarter shipments compared to 44.8% in the Q1 of fiscal 2023 and $46,300,000 in the prior quarter. I'd now like to hand the call over

Speaker 1

to Doug. Please go ahead, Doug.

Speaker 3

Thank you, Didier. GSI reported a net loss of $5,100,000 or $0.21 per diluted share on net revenues of $5,600,000 for the Q1 of fiscal 2024 compared to a net loss of $4,000,000 or $0.16 per diluted share on net revenues of $8,900,000 for the Q1 of fiscal 2023 and a net loss of $4,000,000 or $0.16 per diluted share of net revenues of $5,400,000 for the Q4 of fiscal 2023. Gross margin was 54.9 percent in the Q1 of fiscal 2024 compared to 60.2% in the prior year and 55.9% in the preceding 4th quarter. The year over year decrease in gross margin in the Q1 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 Q1 of fiscal 2024 were $8,200,000 compared to $9,300,000 in the Q1 of fiscal 2023 and $6,900,000 in the prior quarter.

Speaker 3

Research and development expenses were $5,200,000 compared to $6,600,000 in the prior year period and $5,000,000 in the prior quarter. Selling, general and administrative expenses were $3,000,000 in the quarter ended June 30, 2023, compared to $2,700,000 in the prior year quarter and $1,900,000 in the previous quarter. We that through June 30, 2023, we have incurred research and development spending in excess of $140,000,000 on our APU product offering. Q1 fiscal 2024 operating loss was $5,100,000 compared to an operating loss of $3,900,000 in the prior year period and an operating loss of $3,900,000 in the prior quarter. Q1 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, other expense and a tax provision of $60,000 for the same a year ago.

Speaker 3

In the preceding 4th quarter, net loss included interest and other income of $101,000 and a tax provision of 191,000 Total first quarter pre tax stock based compensation expense was $820,000 compared to $638,000 in 1 quarter a year ago and $515,000 in the prior quarter. At June 30, 2023, the company had 27 point $7,000,000 in cash, cash equivalents and short term investments compared to $30,600,000 in cash, cash equivalents short term investments at March 31, 2023. Working capital was $32,100,000 as of June 30, 2023, compared to $34,700,000 at March 31, 2023 with no debt. Stockholders' equity as of June 30, 20 $23 was $48,600,000 compared to $51,400,000 as of the fiscal year ended March 31, 20 '23. During the June quarter, the company filed a registration statement on Form S-three so that the company would be in a position to quickly access markets and raise capital if the opportunity arises.

Speaker 3

Operator, at this point, we'll open the call for Q and A.

Operator

Thank you. We will now be conducting a question and answer session. A confirmation tone will indicate your line is in the question Our first question comes from Nicky Doily, Nederland and Company. Please sir go ahead.

Speaker 4

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

Speaker 3

Yes. It's really related to product mix. We do our best effort What we believe the revenues are going to be during the quarter. But obviously, with only about a third or so of the quarter, look to the beginning of the quarter, we have to estimate where the revenues It's going to come from. And it's strictly

Speaker 1

tied to product mix, nothing more.

Speaker 4

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

Speaker 3

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

Speaker 4

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 the specific GPT application. Can you just expand on how that's done? How your APU differentiates from CPUs and GPUs on the market?

Speaker 4

Is it entirely to do with the ability to do computations at the bit level, that was my understanding. Yes. I mean, to each other would be great.

Speaker 1

Yes. First of all, GPU has a very, very small cash. And I think it's good for the graphic processing. But when you talk about the huge parameter in the large language model, they can only do the fraction of what they can do from the top point of view. And in the GPU, we have a huge, Huge memory inside the chip.

Speaker 1

And we calculate the top, it's strictly around how we can support the processing with our memory, okay. That's how we come out with the TAF. So that's why we have average is TAF, Okay. So I hope I answered your question.

Speaker 4

Okay. And if I could just sneak one more. I think in the past, you talked about the cost of Gemini 2 is about 2,500,000 Is that still the case? And is that entire tape out cost behind us or it's still ongoing?

Speaker 1

So

Speaker 3

the $2,500,000 is a tape out cost. So we will have A tape out, the expense could hit later this quarter or the early part of the October quarter. But yes, that's just the tape out quarter. We've incurred, as we said in our comments, probably in excess of about $140,000,000 develop This product line and that's for G1 and G2.

Speaker 4

Great, thanks.

Speaker 1

Just one comment. We published a white paper on our website and we have a further discussion on You know, via APU is good for the large language model. So if you're interested, look at www.gfztechnology.com.

Operator

Our next question came from Luc Boren, Pravet Investor. Please sir, go ahead.

Speaker 5

Thanks. So in terms of that study, did you mention that, that was projecting a 5 nanometer architecture for the you have a study about comparing with GPUs and MP performance?

Speaker 1

Correct.

Speaker 5

And I'm supposing based on your understanding of the engineering, the physics of your APU architecture that you projected that is feasible. And is that the case? And can you project even further to say that, yes, there is a limit that's lower and in terms of reducing to even more dense architecture?

Speaker 1

Yes. We picked phenomena because at This moment, the sale of our processor is either at 5 or 4 nanometer. So we want to have Apple to Apple comparison, we pick the planometer 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 just same as everybody else.

Speaker 5

Okay. Yes. So that is the tentative plan is to make the leap basically from your current, I think you said 16 with Gemini 2 all the way to the 5 for Gemini 3?

Speaker 1

Yes. No, no. Gemini 3 is to be determined. We picked 5 nanometer just because everybody else is on the 5 nanometer. So it's a fair very recently.

Speaker 6

Right.

Speaker 1

And so, yes, that 5 nanometer is

Speaker 2

picked just for a comparison for the study because that's what, As Helene just said, that's what the GPUs are on. It's 5 nanometer. So we wanted to do a straight comparison on technology. That does not mean Gemini 3 would be on that technology. It could be something more aggressive.

Speaker 5

Okay. Yes. So not a limit point? Correct. Excellent.

Speaker 5

And in terms of the yes, 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 and machine vision for both for us like real world vision, talking about EVs, autonomous vehicles and kind of referencing the Tesla earnings call saying that they're buying as many NVIDIA GPUs as they can get their hands on. And your earlier references of being able to apply the APU to that market as well as yes, more of the abstract machine vision, drug discovery and genetic medicine, things like that. Are you seeing still some more damages?

Speaker 2

Yes. So I mean the advantage yes, the answer is our Gemini 1, We understood was not a fit for what you talked about ADAS. Gemini 2, we anticipate to be a better fit just because of the lack of an FPGA on the board with the Gemini 2. But the fundamental unique architecture is going to be the same, which is The fact that we're doing the computation or the search on the memory bit line in place. And so we're not going off chip to fetch the data and then going back and rewriting the data.

Speaker 2

So that's the fundamental unique architecture that we have is regardless of the market and is available or There with Gemini 1 and Gemini 2.

Speaker 5

Awesome. Yes. Yes, I just wanted to get that clarification about Since, yes, we talked about the performance being kind of or for GPUs being apt for visual processing. So I

Speaker 1

want to get that clarification about

Speaker 5

the more broader kind of machine vision, visual processing markets there. Yes, that's great. I think I have one more question. Yes, definitely probably all for getting 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 you have that TAM you all can address. Yes, definitely good to hear that you're putting there's more traction through that pathway.

Speaker 5

And one just Kind of funny curiosity, I've noticed the name Gemini associated with accelerated computing, most recently and most prominently with Google. And it always made sense to me in terms of parallel processing. You had the name Gemini historical reference. But wondering, yes, ThinQ and Google have now also adopted Gemini. I'm wondering if that is at all a encroachment on your intellectual any of your trademark or if you find that to just be a kind of a humorous affirmation since you're the 1st Gemini?

Speaker 3

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

Speaker 5

Okay. That makes sense. Okay. So, is there has anything shifted? I'm not sure if you've actually crunched numbers, but in terms of, you have your TAM and yes, Sam and these new focuses on the large language models.

Speaker 5

Yes, how do you see kind of the concrete your concrete addressable market projections updated at this in terms of timeline and size?

Speaker 2

Yes. So we're still working on those TAMs for that. And there's different segments, right? You have The retrieval and you have the generative. And so those are 2 different areas.

Speaker 2

We can certainly address the retrieval now with Gemini 1 and Gemini 2. And we certainly feel for the generative side, it's going to be more with Gemini 3. But yes, we're working on those TAM SAMs No. They're just not available yet.

Speaker 5

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

Speaker 2

Thank you, Luke.

Operator

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

Speaker 4

Yes. Hi, guys. Couple of questions for you. One, just on the last answer, you were talking about Gemini 1 and Gemini 2 addressing retrieval. So you mean queries there?

Speaker 4

And when you say addressing generative, are you talking about training or just clarify that a little bit?

Speaker 2

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. And that's why we talked about pairing up with HBM III for that. And so that's more on the generative side.

Speaker 4

Okay. So training, Jim, I'd say.

Speaker 1

No, no, no, no, no.

Speaker 2

Inference. It's still inference, yes. It's not training.

Speaker 4

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 3, line with. What is the current tape out cost?

Speaker 4

So I know that in our processor is more like a memory, so it might be less expensive. But what do you think a takeout cost of 5 nanometer would be now?

Speaker 1

Well, the nanometer, the mass cost itself above $15,000,000 One side, to have a design like a planometer, we probably need to have $100,000,000 for the design. So what we are doing right now is that we really are looking for the partner. We are not trying to do it ourselves. So

Speaker 5

Okay.

Speaker 4

And then I just wanted to talk about the capital situation. You've now got a registration statement in place. Unfortunately, you missed the big run off 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?

Speaker 3

Well, we have looked into the sale of the building, and we haven't decided to do that yet, but that still is an Property values are significantly higher than when we purchased the building Years ago. And then this is an opportunity that we have considered and we've discussed it with the Board, but no decision as of yet has been made to sell the building.

Speaker 4

Got you. Okay. And then just on the Nokia business, that if I remember correctly, you guys were in Now at this point, they're pretty old Nokia 7970 and 7950 routers. I don't even see any reference anymore to the 50. What's going on there?

Speaker 4

How much lead time would you get if they were end of life in that? Would there be some kind of lucrative end of life revenue that you might get out of that, etcetera. Just give us a little feeling for your understanding of where you are with the Nokia business?

Speaker 2

Sure. Yes. So as you said, it's in the 7,750 and 7,950 platforms there. And they have extremely long life cycles, as we've been seeing. We get a 12 month rolling forecast from Nokia.

Speaker 2

And so far and that's as far as they go and the 12 months still looks healthy. What they did do a while back as they did what's called a midlife kicker to try and give a little bit more performance to those existing systems. And what that meant for us is that, it went from a 72 megabit density into a 144 megabit, density part for that midlife kicker. And so the ASPs are obviously higher on the larger designated parts. 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 the quantity.

Speaker 2

So at this point, it's still going. We still have the 12 month forecast that looks healthy, and that's much visibility as we get.

Speaker 4

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

Speaker 5

rate year going forward?

Speaker 2

So the lead times have become more normalized. The pricing or the costs have not. So the price increases that were subjected to us, which in turn forced us to raise prices to our customers, They're still there. And so 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.

Speaker 2

Lead times have Come down to a more normalized area.

Speaker 4

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

Speaker 3

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

Speaker 1

Great. Thank you.

Operator

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

Speaker 6

Thank you. It's George Gaspar. Just again, I'd like to deal 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 on the need to exercise financing requirement?

Speaker 3

Well, at this point, given the materials we've discussed with the Board, this fiscal year, we'll Certainly burn some cash and maybe $12,000,000 to $13,000,000 if the revenue numbers hold up. And if the revenue numbers hold up next Sure. We could start turning the corner and actually having more cash at the end of fiscal 2025 than at the end of fiscal 2024.

Speaker 6

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

Speaker 3

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

Speaker 6

It is. Okay. All right. Thank you. Thank you.

Operator

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

Speaker 1

Thank you all for joining us. We look forward to speaking with you again when we report our Q2 fiscal 2024 results. Thank you.

Operator

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

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GSI Technology Q1 2024
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