GSI Technology, Inc.

Please stand by, we're about to begin. Ladies and gentlemen, thank you for standing by. Welcome to GSI Technologies' first quarter fiscal 2021 results conference call. At this time, all participants are in a listen-only mode. Later, we will conduct a question and answer session. At that time, we will provide instructions for those interested in entering the queue for the Q&A. Before we begin today's call, the company has requested that I read the following Safe Harbor statements. 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 results 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 30, 2020. at the request of DSI 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.

Welcome, everyone, and thank you for joining us to review our fiscal quarter, our first quarter, 2021. Financial Results. CSI has developed a truly disruptive technology with the Gemini APU processor. We are now working on getting the message out to industry and customers on the APU advantage in several social applications, including soft discovery, facial recognition, air military, and defense applications. To do that, We published a white paper in April documenting the performance benchmark on a one billion item database. Recently, the Lee-Lean Group, a leading publisher of semiconductor technology analytics, supported our funding in their recent white paper on the APU. This paper describes in detail Gemini's revolutionary architecture and give an overview of the competitive advantage with performance examples. Both white papers are available on our website. Recognition is growing for in-memory computing as the ultimate solution for analyzing massive datasets that overwhelm from human processes. The APU design with the computation function Closer to the Data, solved the volume model limitations related to having separate compute and memory functions. Beyond performance, Gemini's hardware and software solutions also enable tremendous scalability. With the Gemini One chip installed on the PCIe board that plugs into the standard server, which we call the LiDAR-G board, We have shown that Gemini has the linear scalability to manage 40 billion records and beyond by combining multiple systems with multiple boards. The Lean Group's five levels highlight Gemini's advantage over competitive solutions. In particular, GPUs and GPUs designed to have a high compute capability working with smaller data sets. as limitations when working with large data sets. As the data sets can now exceed a billion items, GPU and GPU solutions require more installed hardware, PC and servers, as well as increased power to keep pace with the expansion of data and needs of search applications. This drives out the total cost of ownership, making this solution very expensive. In contrast, Gemini's architecture is the optimal solution for search applications or massive data sets. Each of Gemini's millions of processors can load data directly from its own chip memory, allowing for much greater data flow. By removing the volume processor-sized memory bottleneck, Gemini outperforms standard processors by 100 times or more reducing power consumption by 70% in such applications. Gaming assistants use less hardware, four smaller footprints, and use significantly less power. This reduces the overall total cost of ownership while delivering better outcomes. As GSIs enter new markets, they engage with customers unfamiliar with our company and our technology We face business challenges magnified by COVID. Resources such as industry conferences and on-site training workshops, which could be a normal business process, to build a sales pipeline, sometimes available under COVID restrictions. We are experiencing these limitations everywhere, in California and North America, Europe and Asia, and with government agencies. Across the board, it's more challenging to connect and build the brain awareness we need to make inroads for Gemini One. As a result, the goals we have set for Gemini One in calendar year 2020 allow more challenges to achieve. Our legacy business is also impacted by COVID restrictions this year. Overall, our business visibility is reduced in the environment. That said, We anticipate improvement from last year in the second half of our fiscal year. GSI remains on solid financial footing with a strong balance sheet with $69 million in total cash balance with low debt. Despite these challenges, we are making progress with customers in the field of education and have shipboards to early adopters. We expect to see development this year in the field search applications, soft discovery, facial recognition, and the various military and defense applications. As I stated earlier, we have limited visibility at this time that affects design width and model sales of the NIDA keyboard by year-end 2020. In the meantime, our AI team is focused on hardware and software improvements on several fronts, First, we are continuously improving Gemini One to increase speed and lower power consumption. Therefore, we anticipate providing further improvements to Gemini One's performance. Second, we are developing the software to provide customers with the tools to develop readily adaptable solutions. Soon, we will launch a compiler that will make it easier for customers and the researchers to write their own software. The compiler is for GSI-developed Python-based programming language with parking APU-specific instructions. We are also developing a software debugging tool called NanoSim that allows a programmer to automatically generate APU machine code and see simulator from their own high-level language program. This software will also allow programmers to visualize and manipulate the APU processing array and the instructions interactively. Nanosyn is in the early stage of design and is undergoing internal prototyping. We have completed developing a few product-specific libraries, including GSL, short for GSI Search Library. TSL is used to support search functions inside a database. For example, TSL supports a function like Heiming-Kehler's label, or KNN, used on billion-scale terminology search, as well as Tanimoto KNN to be used on molecular search. We increase awareness of the APU, access expert knowledge to improve our solutions, and develop an ecosystem for our technology. GSI's Fractose project is collaborating with SWAP, the National Science Foundation Center for Space, High Performance and Recycling Computing. The partnership will give the GSI team access to a wide variety of AI technology experts. Lastly, other CEOs and larger shareholders of GSI I am highly invested in delivering on our goal to monetize the investment we have made in the APU. My team is fully committed to this goal as well. We are at a crucial point in our evolution from a legacy SM company to an innovator in the ASM conductors. We understand the responsibility we have to our shareholders to execute our long-term strategy successfully. 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 in further detail. Please go over, Didier.

Thank you, Lee-Lean. I want to expand on some of the points covered by Lee-Lean. GSI's Fractals project partnership with Shrek brings resources to GSI that will help us increase our solution performance and expand our sphere of influence in this community. Shrek is made up of universities, government agencies, and companies from the technology, military and defense, and space sectors. Fractal's project is focused on Gemini solutions, and in working with members of Shrek, we will be able to leverage many resources from this community. The focus in this partnership is to advance our radiation-hardened, radiation-tolerant product lines and the radiation-tolerant APU. This new collaboration has already benefited GSI in creating new relationships working with us to improve the LITA-G board functionality. In the Lindley Group white paper, it references the Wiseman Institute's work. The paper discusses the APU's performance using Knier's neighbor, also known as KnN, for biomedical and pharmaceutical research identifying molecules based on their characteristics. KANN is a search algorithm that is flexible and easy to implement for classifying data based on similarity, making it highly suitable for analyzing large datasets like molecular databases. However, the high computational cost of performing this search with CPUs and the need for lots of memory in performing a KANN search limits its use in this application. GSI Technology is excited to be helping researchers at the Wiseman Institute search for COVID-19 treatments. The Wiseman Institute performs KNN searches using a LIDA-G board loaded with 38 million record database of molecular fingerprints. Given the APU's flexible design, they can load the database in different ways, such as 512 bits per fingerprint in 2.3 gigabyte or 1024 bits per fingerprint in 4.6 gigabytes. The LIDA-G board can even handle larger fingerprints, such as 2048 or 4096 bits that would take too long to run on CPUs. The APU's performance truly stands out as the databases become larger and more complex. The 680 million entry database can be formatted into a data set of 43.5 megabytes of 512-bit fingerprints and loaded onto four LIDA G-boards, taking only 4.5 seconds to be searched. By comparison, the dataset would require more than one hour when processed on a CPU-based system. Another time-saving feature of the APU is the ability to process multiple queries in parallel on the data. This ability improves batch searches' performance also. This is an important differentiator for the APU, which can do four parallel searches as compared to a GPU, which cannot do parallel searches but instead do them in batches sequentially. For example, on the data set of 38 million molecular fingerprints discussed above and loaded on one LIDA Gboard, a single query will take 1.1 seconds. The card can process 100 different queries at the same time for molecules in 1.41 seconds, or 71 queries per second, a function that the CPU cannot perform and the GPUs are not as efficient. The Weizmann Institute uses leaded G-boards to accelerate the discovery of active compounds that may prove useful in COVID-19 treatments. The Gemini One APU is being used by researchers I'm sorry, researchers to identify compounds structurally similar to Redemzivir, an antiviral drug recently shown to be effective in treating severe COVID-19 patients. Wiseman researchers with Redemzivir as the query molecule are using the Gemini APU on the LIDA G-Board to perform very rapid similarity structure searches in their in-house database in their search for compounds to develop new treatments for COVID-19. As Lee-Lean stated earlier, we are making Gemini One faster and more power efficient. We target having an enhanced version available later this year. The AI team is also moving ahead on the next generation of the APU. We are in the early stages of building awareness of our new chip's capability and functionality. Shifting to the sales breakdown for the first quarter, sales to Nokia were $1.8 million, or 24.9% of net revenues, compared to $6 million or 45.7% of revenues in the same period a year ago and $2.4 million or 28.3% of net revenues in the prior quarter. Military defense sales were 30.9% of first quarter shipments compared to 21% of the shipments in the comparable period a year ago and 30.9% of shipments in the prior quarter. Sigma Quad Sales were 46.3% of first quarter shipments compared to 69.7% in the first quarter of fiscal 2020 and 44.7% in the prior quarter. I'd now like to hand the call over to Doug. Go ahead, please, Doug.