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GSI Technology Takes Home First Prize in MAFAT Radar Challenge

GSI Technology Takes Home First Prize in MAFAT Radar Challenge

SUNNYVALE, Calif., Jan. 06, 2021 (GLOBE NEWSWIRE) -- GSI Technology, Inc. (Nasdaq: GSIT), a leading provider of high-performance memory solutions for the networking, telecommunications and military markets, and developer of the Gemini® Associative Processing Unit (APU) for artificial intelligence, today announced that it has been awarded first prize in the .

GSI Technology was awarded the $25,000 Grand Prize after passing all the competition’s formal and technical eligibility tests. The participants’ challenge consisted of distinguishing between humans and animals in radar signal segments. The challenge provided a rare glimpse of radar signals and its challenges. GSI Technology overcame this challenge through visualization and data manipulation using classic CNN (convolutional neural network) models. For more insights on the challenge, read GSI Technology’s .

“We are thrilled to be awarded first prize in this very exciting competition, and we would like to thank the MAFAT team for the well-structured material and informative resources they provided to the contestants,” said Lee-Lean Shu, GSI Technology's Chairman and CEO. “I’d also like to acknowledge the leadership of Daphna Idelson and the GSI team throughout this challenge. This project offered a valuable chance to work with radar signals and their unique challenges—and proved that the GSI team can tackle these challenges with our highly adaptable APU software. This award highlights our chip design capabilities and our strength in software solutions for complex, novel applications. We look forward to participating in the next MAFAT Challenge.”

This year’s MAFAT Radar Challenge focused on the classification of living, non-rigid objects detected by doppler-pulse radar systems. This was the second competition in the MAFAT Challenge series in the field of data science sponsored by the Israeli Ministry of Defense Directorate of Defense Research and Development. The competition was fully open to the academic and industry sectors, as well as to the general public. This effort highlights the value of AI in helping glean intelligence from basic sensor input, which normally is used to detect distance of objects being used to classify the objects themselves.

The competition hosted more than 1,000 registered participants and received more than 4,300 submissions. During the competition, participants received a training data set containing 6,656 radar segments, labeled as either animals or humans, in addition to a supportive (auxiliary) data set containing 49,071 segments. Participants trained their machine learning predictive models based on the training data, and were asked to make predictions for this binary classification task on untagged data (the test set).

ABOUT GSI TECHNOLOGY

Founded in 1995, GSI Technology, Inc. is a leading provider of SRAM semiconductor memory solutions. GSI's newest products leverage its market-leading SRAM technology. The Company recently launched radiation-hardened memory products for extreme environments and the Gemini® APU, a memory-centric associative processing unit designed to deliver performance advantages for diverse AI applications. The Gemini APU's architecture features parallel data processing with two million-bit processors per chip. The massive in-memory processing reduces computation time from minutes to milliseconds, even nanoseconds, while significantly reducing power consumption in a scalable format. Gemini excels at large (billion item) database search applications like facial recognition, drug discovery, Elasticsearch, and object detection. Gemini is ideal for edge applications with a smaller footprint and lower power consumption, where rapid, accurate responses are critical. Headquartered in Sunnyvale, California, GSI Technology has 172 employees, 114 engineers, and over 90 granted patents. For more information on the company and our products, please visit our website at

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GSI Technology, Inc.

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408-331-9802



EN
06/01/2021

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