GSI Technology, Inc. (GSIT) CEO Lee-Lean Shu on Q1 2022 Results - Earnings Call Transcript

GSI Technology, Inc. (NASDAQ:GSIT) Q1 2022 Earnings Conference Call July 29, 2021 4:30 AM ET

Company Participants

Lee-Lean Shu - Chairman, President & Chief Executive Officer

Didier Lasserre - Vice President of Sales

Douglas Schirle - Chief Financial Officer

Conference Call Participants

Jeff Bernstein - Cowen

Operator

Good day ladies and gentlemen and thank you for standing by. Welcome to GSI Technology's First Quarter Fiscal 2022 Results Conference Call. [Operator Instructions] 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 July 29, 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 everyone and thank you for joining us to review our first quarter 2022 financial results. First quarter revenue improved year-over-year and sequentially due to a higher sales to Nokia our largest customer. There was increased demand for all SigmaQuad products which improved gross margin and narrowed our operating loss.

We ended the quarter with over $55 million of liquid assets more than sufficient funds to achieve our goals of building successful business for new products. The net revenues from our legacy SRAM business continue to support our new product categories. Primarily radiation-tolerant devices and the Gemini APU solutions. We ship our first radiation-tolerant devices in the first quarter and increased our better customer engagement for Gemini-I systems.

Our effort to build market recognition of this new product categories yield several promising results in the first quarter. First winning first place in the MAFAT Challenge has opened the door to a Israeli prime contractor with whom we successfully demonstrated Gemini's overall value proposition for Synthetic Aperture Radar or SAR applications leading to a proof-of-concept engagement for SAR systems.

Second, the partnership with Space Micro for the Phase I, NASA Small Business Innovation Research or SBIR program has gotten underway. We expect to submit the Phase II proposal to develop an optimum real-time data sorting inference processing unit board for Earth observation mission later this year.

In the third -- in the first quarter we shipped our first radiation-tolerance SRAM devices as essential step in getting space heritage for our radiation-harden and tolerant devices. Didier will provide detail on our progress in each of these new engagements in his remarks to follow.

Last week, AWS launched OpenSearch 1.0 the first production-ready versions of OpenSearch a project AWS first introduced in April 2021. The OpenSearch project is a community driven open source search and analytics suite derived from open source Elasticsearch and the Kibana data visualization dashboard software. Since the spring we have been working with AWS on this project demonstrating GSI's Elasticsearch K-NN plugin. Elasticsearch was initially designed as a text and a document search engine.

The Gemini Elasticsearch K-NN plugin or extension extends Elasticsearch's ability to search beyond just text. The plugin opens the door to other data types like image, video, audio, any data type that can be represented as a compact semantically rich numeric vector. Vectors can be used with search for the most similar items or nearest neighbors to a query, making a seller in multiple applications such as video search, face recognition, natural language processing and the recommendation systems.

Our extension provide a high-performance, low latency, low power, building-scale vector similarity to search solution that allows user to combine traditional text filters with vector search queries. Here's the problem we solved. Core Elasticsearch use Match-O functionality which makes it too slow to handle the large-scale initial retrieval step in the vectors in an R&D search pipeline. This limits Core Elasticsearch to Scoring fast enough on a small filtered set of vectors.

Instead of using a Sox-T Match-O search the GSI plugin perform approximately nearest neighbor or K-NN vector similarity search. This allows the GSI potentially to scale through billions of documents and handle the essential initial retrieval step in the search pipeline. Multi-model search, raw image and text can combine to former powerful search. It's a rapidly emerging trend, online fashion and the home design use multi-model search because they really heavily run video search since style is often difficult to describe using text.

In addition to video search, text search is also required because production product information like item description, category and brand is generally used to filter the return result as part of the video search. A solution that allows for multi-model search is needed. Open source search library do not handle multi-model search well. The popular open-source vector search library, part self structure space and NMS library are the – DS labor vector search for support for efficient data filtering.

For example, it could be difficult to use texture filters to filter the results previously returned from DS labor vector search. GSI solved this problems with its largest search parking by allowing multi-model searches to be performed efficiently and effectively. Our extension provide a high-performance, low latency, low power solution that combines traditional Elasticsearch, text filters with vector search queries, solving the imaging and text search problem. And our solution is compelling, because we require fewer resources for better outcome.

After successfully proving that, the Gemini APU significantly accelerated vector search performance Amazon OpenSearch service included GSI as one of its service partner in the project. AWS recently released Version 1.0 of OpenSearch for users of Elasticsearch and developers, building products and service-based on Elasticsearch.

AWS has select partner that offer software and extension that support OpenSearch in various applications like our Elasticsearch k-NN plug-in that expands Elasticsearch's ability to search beyond just text. We are excited to be a part of this team.

We are in the early stage of developing this opportunity. And we still have a lot of work to do. We still need to finalize business model and are setting up a data center in Silicon Valley with the Gemini APU server in the GSI cloud to connect to the AWS data center nearby.

Once the setup is complete and successfully demonstrated, we could now install more servers and get better users by year-end. And if all goes well, open up to all customers in calendar 2022. Long-term, we may install Gemini APU servers at data centers in many locations around the world.

The first quarter has been productive for GSI. My team, here in California as well as our teams in Israel and Taiwan, are all highly committed to our goal of lending customers and building successful business for our radiation-tolerant devices and the Gemini APU. We are progressing and remain optimistic that we will be successful in executing our long-term strategy. I sincerely thank you for your support as fellow GSI shareholders.

Now, I hand the call over to Didier, who will discuss our business performance in further detail. Please go ahead, Didier.

Didier Lasserre

Thank you, Lee-Lean. I would like to provide an update on two product categories: our radiation-tolerant or Rad-Tolerant chip that shipped in the June quarter, and our progress on the APU in government projects.

Last quarter, we shipped our first Rad-Tolerant SRAM for an initial satellite flight expected to occur at the end of this calendar year. If the initial flight is successful, the larger satellite constellation build is expected to start later in calendar 2022.

We now anticipate a companion satellite project that will use the same class of Rad-tolerant SRAM. This is a substantial opportunity for us and also brings the key benefit of establishing heritage for our Rad-hard and Rad-tolerant SRAMS in space. We anticipate that having heritage will be a growth catalyst for this product category.

Shifting gears to the APU. As we previously announced we partnered with Space Micro for our Phase 1 NASA SBIR program. We believe this project can serve as a catalyst for APU sales in this sector.

In this project, the APU will function as the main engine for a single-board computer in space, which for this project is being referenced as an IPU or an inference processing unit. Phase 1 is officially underway after the initial kick-off meeting, and is expected to be completed before year-end.

The next step is to submit a Phase 2 proposal, which will probably happen in early calendar 2022. This is an exciting opportunity to put APU in the hands of multiple government agencies and prime contractors for space applications.

Lastly, another exciting APU development is the outcome of winning the MAFAT Challenge at the end of last year. Since then, MAFAT has gotten to know GSI and the team has had the opportunity to work on our APU with MAFAT to demonstrate the scope of functionality of the device.

Notably, we have demonstrated the APU's capability to perform SAR operations that our APU functionality exceeds that of the CPU and the GPU by a factor in some cases a hundred times better, including lower power and lower overall system cost.

As a result, we are now working with an Israeli defense prime contractor to deliver proof-of-concept servers to perform SAR operations. We expect to deliver the servers in early calendar 2022. If the effort is successful, we hope to leverage the success into other projects.

Shifting to the sales breakdown for the first quarter of fiscal 2022 sales to Nokia were $3.8 million or 42.7% of net revenues compared to $1.8 million or 26.9% of net revenues in the same period a year ago and $2.8 million or 36.5% of net revenues in the prior quarter.

First quarter fiscal 2022 net revenues include orders for buffer stock shipped to Nokia, amounting at approximately $1.1 million that were made in anticipation of continued market tightness.

Military defense sales were 20.1% of first quarter shipments compared to 30.1% of shipments in the comparable period a year ago and 22.5% of shipments in the prior quarter. SigmaQuad sales were 63.6% of first quarter shipments compared to 46.3% in the first quarter of fiscal 2021 and 52.9% in the prior quarter.

I'd now like to hand the call over to Doug. Go ahead, Doug.

Douglas Schirle

Thank you, Didier. We reported a net loss of $4.2 million or $0.17 per diluted share and net revenues of $8.8 million for the first quarter of fiscal 2022, compared to a net loss of $6.1 million or $0.26 per diluted share and net revenues of $6.6 million for the first quarter of fiscal 2021 and a net loss of $5 million or $0.21 per diluted share and net revenues of $7.7 million for the fourth quarter of fiscal 2021.

Gross margin was 54.4% compared to 46.1% in the prior year period and 50.2% in the preceding fourth quarter. Improvement in gross margin was primarily due to changes in product mix sold in the quarter, which reflects an increase in higher margin SigmaQuad sales as compared to the prior periods.

Total operating expenses in the first quarter of fiscal 2022, were $9.1 million, compared to $8.7 million in the first quarter of fiscal 2021 and $9.1 million in the prior quarter. Research and development expenses were $6.1 million, compared to $5.8 million in the prior year period and $6.1 million in the prior quarter.

Selling, general and administrative expenses were $3 million in the quarter ended June 30, 2021, compared to $2.9 million in the prior year quarter and $3 million in the previous quarter.

First quarter fiscal 2022 operating loss was $4.4 million, compared to $5.7 million in the prior year period and $5.3 million in the prior quarter. First quarter fiscal 2022 net loss was $4.2 million, compared to a net loss of $6.1 million in the first quarter of fiscal 2021 and $5 million in the fourth quarter of fiscal 2021.

First quarter fiscal 2022 net loss included interest and other expense of $20,000 and a tax benefit of $172,000, compared to interest and other income of $106,000 and a tax provision of $487,000 in the prior year, primarily resulting from the settlement of a tax audit in Israel fiscal years 2017 through 2019.

In the preceding fourth quarter, net loss included interest and other expense of $21,000 and a tax benefit of $304,000. Total first quarter pre-tax stock-based compensation expense was $823,000, compared to $755,000 in the comparable quarter a year ago and $753,000 in the prior quarter.

At June 30, 2021, the company had $51.5 million in cash, cash equivalents and short-term investments and $4.3 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 $54.8 million, as of June 30, 2021 versus $56 million at March 31, 2021 with no debt. As of June 30, 2021 stockholders' equity was $73 million, compared to $75.6 million as of the fiscal year ended March 31, 2021. For the upcoming second quarter fiscal year 2022, our current expectations are net revenues in the range of $6.9 million to $7.9 million with gross margin of approximately 51% to 53%.

Supply chain shortages and the impact of rising COVID infections in Taiwan that have recently impacted the operation of our manufacturing partners are expected to impact our fulfillment of sales in the near-term and possibly for the remainder of calendar year.

Operator at this point, we'll open the call to Q&A.

Question-and-Answer Session

Operator

[Operator Instructions] First we'll go to Jeff Bernstein from Cowen. Your line is open.

Jeff Bernstein

Hi, guys. So I just wanted to make sure I understood the news with regard to the AWS open sourced OpenSearch partnership. So just correct me where I'm wrong, if I haven't understood. So AWS is offering an open sourced search capability in their facilities. And without your product and software extensions to use the APU that open sourced search capability was pretty limited. And I think you said you were bringing the capability to do a lot more things and add the efficiency both power efficiency and speed of APU.

If I've got that right, where are these APUs going to run your software extensions? Are they going to run in the Amazon cloud and be a facility for people to use, or people going to preprocess in the Amazon cloud and then buy APUs from you to run search on their premises, or how does all this work? And most specifically, how does this relationship convert to APU sales and revenue for you?

Lee-Lean Shu

Yes. Our user will use Amazon cloud. They will feel like they are in Amazon cloud. But when they send the request to the Amazon, the query will send to GSI cloud and then we will return the result back to Amazon. And so for user point of view they are operating in the Amazon environment. We do acceleration and we provide the search function to it.

Jeff Bernstein

I see. So in this case you will be offering a service where you build your own GPU – I'm sorry your own APU…

Lee-Lean Shu

APU.

Jeff Bernstein

Cloud with the Gemini APUs. And then you use that to accelerate and also expand the capabilities of search that would have been done in the AWS open sourced search cloud?

Lee-Lean Shu

Yes perfect. Yes. I – yes you've got it.

Jeff Bernstein

Got it. So in the positive of this is that Amazon is becoming very aware of what your capabilities are as a result of this or at least part of Amazon is. And customers who want to use this facility may help you build a standalone business of your own in providing an accelerated cloud, a little like how some of the – like IBM is trying to do with Quantum.

Lee-Lean Shu

Yes, you are absolutely right. We – in the meantime, we are discussing and working with several other companies the vector search space as you will just like Amazon. And we are working on it. And hopefully, Amazon is the first one and then – and open the door for the much bigger business for us.

Jeff Bernstein

Understood. Thank you.

Operator

[Operator Instructions] And we have no further questions in the queue.

Lee-Lean Shu

Thank you all for joining us. We look forward to speaking with you again, when we report our second quarter fiscal 2022 results. Thank you.

Operator

And that does conclude our call for today. Thank you for your participation. You may now disconnect.