Good afternoon, and thank you for joining us to review our second quarter fiscal 2023 financial results. Revenue in the second quarter of fiscal year 2023 grew year-over-year by nearly 15% to $9.0 million, the midpoint of our guidance range. Revenue growth was primarily due to increased sales of high-performance SRAM and the shipment of radiation-hardened SRAMs in the quarter. As a result, our gross margin rose by 900 basis points year-over-year to 62.6%. Higher gross margin, along with flat operating expense year-over-year led to a lower operating loss for the quarter. Net loss for the second quarter narrowed to $3.2 million. As of September 30, 2022, GSI Technology's cash balance, which includes cash and cash equivalents as well as short and long-term investments was $38.9 million. We have a number of items in the file with Gemini-I, but our team is primarily focused right now on developing two near-term markets for the APU. The first is Synthetic Aperture Radar or SAR, image processing acceleration; and the second is fast vector or neural search optimized for big data applications. Let me help you understand why these two markets have been prioritized. At the end of September, we announced that our Synthetic Aperture Radar image processing acceleration system using GSI's APU was approved for use by Elta, a subsidiary of Israeli Aerospace Industries. It is a chance to showcase the technological superiority of our APU and will open future opportunities for GSI in the view of accelerating SAR image formation with new customers. The features that make our SAR image processing acceleration system solution very attractive are: one, scalability; two, low power consumption; and three, portability. First, with respect to scalability. The scalable APU architecture allows expanding to multiple boards on servers for added performance and redundancy without specialized links. The APU platform has the capability to stack servers together and bring near real-time capability to time-consuming, compute-intensive processes. Second, with respect to lower power consumption, GSI has demonstrated using a large area, SAR image process in one second in high-resolution scenarios. That APU used, on average 88% less power than CPU or GPU systems and require significantly fewer servers. And third, with respect to portability, our solution uses one tenth the number of servers than CPU or GPU system, making it small enough to be installed on UAVs and at homes. GSI's SAR can support a broad range of platforms of both from 500 meters quadcopters to 750 kilometers satellite as well as data center deployment. The SAR market is an attractive market where we have existing relationships with prospective targets. Didier will elaborate on the go-to-market strategy for the SAR application in his comments to follow. Our fast vector or neural search product, which I will refer to as our FVS product supports on-prem, hybrid and cloud users. The FVS plugin can be used by customers who are all on-prem and those that may have hybrid storage and a processing in the cloud. For completely cloud-based customers, the Searchium.ai website is live and available for trials. We currently have about 12 users exploring the platform. These users are primarily data scientists evaluating Searchium's capability for their organizations, meaning they lower database and create a front end for the teams to search. The compelling features of the FVS product must be combined with a small hardware footprint and lower operating power consumption compared to traditional large platforms used for search in large complex database. We still have work to do on our end in developing business opportunity for FVS products and scaling the business. The term for the specific searching market we are pursuing is a very large market. Google has estimated more than $300 billion is loss due to search abandonment each year in the U.S. alone. We are providing an accelerator for existing search platforms such as OpenSearch and the Elasticsearch that accelerate the search with lower power and less hardware rather than just adding more traditional computation capability. The Gemini-I plugin expands search capability beyond text and [indiscernible] using a simple Approximate Nearest Neighbor or k-NN vector similarity search. This is Gemini-I's sweet spot in performance and in IT application for the technology. We are ready for production supporting customers on-prem or hybrid solutions. Searchium.ai allow FVS customer to try our product prior to committing their workloads or purchase recovery. We also support full cloud-based customers utilize the OpenSearch database. We have launched Searchium.ai and attract more than 500 visitors and are now working with a dozen opportunities. We will have plugins for large search database. With these three potential customer source to grow this market, on-prem, hybrid and cloud, we are confident that we can see revenue from FVS products in calendar 2023. Last, let me give an update on the compiler stack. We released the [copperhead], the first version of the full stack [indiscernible] compiler for APU co-development for alpha users, and we plan to migrate APU application library over the next few quarters. Currently, copperhead is still in alpha mode and is used exclusively for internal development. We are targeting an end of the year beta release and expect the compiler to be broadly available this calendar year. It is a longer time line than we have originally anticipated, but we are building a fast and efficient tool for use in the operation for [indiscernible] generations. We believe the additional development time will yield better outcome. Now I will hand the call over to Didier, who will discuss our business performance further. Please go ahead, Didier.