GSI Technology, Inc.

Ladies and gentlemen, thank you for standing by. Welcome to GSI Technology's first quarter fiscal 2024 results conference call. At this time, our participants are in a 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 call, the company has requested that I read the following safety 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. These risks and uncertainties are described in the Company Form 10-K, filed with the Securities and Extensions 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 Lian Xu, the Company Chairman, President and Chief Executive Officer with him are Douglas Shirley, Chief Financial Officer, and Didier Lassière, Vice President of Sales. I would like now to turn the conference over to Mr. Xu. 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 toward 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 advancing our growth and innovation objectives. In line with our commitment to land GeminiOne customers, we have moved forward to the demo with two of our sharp targets. Additionally, we add new resource to address the fast vector search market and hone our product for this application. Didier will provide more color on this in his comments. Additionally, I am 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 marked a significant step forward in our product roadmap, enabling us to deliver cutting-edge solutions and drive customer satisfaction. L-Python 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 the Mac OS. The reason L-Python is so fast is because it performs optimization at both a high level and a 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 it can convert the code, which can be useful for specific needs or preferences. Not only is L-Python fast and flexible, but the stack is also usable for other applications. And we believe we could readily create an ecosystem beyond the APU. We are closing in on successfully completing the table of Gemini 2, which is expected to be finalized and sent off to TSMC in the next few weeks. This table is a major achievement and the showcase our commitment to push the boundaries of AI chip technology. GameNet 2 is an extremely complex chip, and the successful completion of this milestone serves as a testament to our talented team's hard work and the expertise. We anticipate sampling the solution during the second half of calendar year 2024. We remain focused on driving innovation delivering exceptional products and leveraging those strengths to foster strategic partnership that will help prepare our company forward. The strategic addition to our team reinforce our commitment to drive growth, fostering partnership, and delivering innovative solutions to our customers. We are excited about the opportunities and the value these individuals will bring to our organization as 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. Now I will hand the call over to Didier, who will discuss our business development and sales activities. Please go ahead, Didi.

Thank you, Lilim. I want to start by addressing a point mentioned earlier by Lilim. 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 scientist 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 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. 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 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. 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 processor's working memory. In the GPT-175 billion 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. 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 have 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 billion model. 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 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 model that power them. 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 tradeoffs between the formats that researchers are investigating. To navigate these tradeoffs, they need a flexible solution. Unfortunately, GPUs and CPUs lack this flexibility and are limited to a small fixed set of data formats. GSI Technologies 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 One 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 SAS revenue source with customized solution 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 Nokia 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, 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. Sigma Quad Sills 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. And now I'd like to hand the call over to Doug.