Good day ladies and gentlemen and thank you for standing by. Welcome to GSI Technology's First 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 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.

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 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 a 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 shipped our first radiation-tolerant devices in the first quarter and increased our customer engagement for Gemini-I systems. Our effort to build market recognition of these new product categories yielded several promising results in the first quarter. First, winning first place in the MAFAT Challenge has opened the door to an 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 first quarter, we shipped our first radiation-tolerant SRAM devices which are an essential step in gaining space heritage for our radiation-hardened and tolerant devices. Didier will provide details 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 version 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 document search engine. The Gemini Elasticsearch K-NN plugin 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 it suitable in multiple applications such as video search, face recognition, natural language processing, and recommendation systems. Our extension provides a high-performance, low latency, low power, building-scale vector similarity search solution that allows users to combine traditional text filters with vector search queries. Here's the problem we solved. Core Elasticsearch uses 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 performs approximately nearest neighbor or K-NN vector similarity search, which allows GSI to potentially scale through billions of documents and handle the essential initial retrieval step in the search pipeline. Multi-model search, combining raw image and text, forms a powerful search capability. It's a rapidly emerging trend, as online fashion and home design use multi-model search because visual style is often difficult to describe using text alone. 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 return results as part of the video search. A solution that allows for multi-model search is needed. Open source search libraries do not handle multi-model search well. The popular open-source vector search libraries, such as Faiss and NMS library, do not provide efficient data filtering. For example, it could be difficult to use texture filters to filter the results previously returned from Faiss vector search. GSI solved this problem with its large-scale search engine by allowing efficient multi-model searches. Our extension provides a high-performance, low latency, low power solution that combines traditional Elasticsearch text filters with vector search queries, solving the imaging and text search challenge. Our solution is compelling because we require fewer resources for better outcomes. After successfully proving that the Gemini APU significantly accelerated vector search performance, the Amazon OpenSearch service included GSI as one of its service partners in the project. AWS recently released Version 1.0 of OpenSearch for users of Elasticsearch and developers building products and services based on Elasticsearch. AWS has selected partners that offer software and extensions that support OpenSearch for 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 stages of developing this opportunity, and we still have a lot of work to do. We still need to finalize the 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 install more servers and attract more users by year-end, and if all goes well, we will 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 landing customers and building a successful business for our radiation-tolerant devices and the Gemini APU. We are making progress 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.

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 the 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 exceed the functionality 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 that 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 to 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.

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, reflecting an increase in higher margin SigmaQuad sales compared to 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 for 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 the calendar year.

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

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, 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 are people 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?

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

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

Yes, perfect. You've got it.

Got it. So 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.

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

Understood. Thank you.

And we have no further questions in the queue.

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.

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