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정규단(Kyudan Jung),김남준(Nam Joon Kim),유현곤(Hyun Gon Ryu),이혁재(Hyuk-Jae Lee) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
The advancement of Large Language Models (LLMs) necessitates managing billions of parameters, requiring cloud-based computational resources. Matrix decomposition and transformation techniques in linear algebra are crucial for efficient processing. However, traditional linear algebra focuses on theoretical proofs and lacks practical applicability. This study selects three key techniques from linear algebra for image compression and inverse transformations, linking theory with practice. This reinforces the mathematical foundation essential for AI development and semiconductor technology, enhancing real-world application performance. This paper demonstrates the practical value of linear algebra in modern technological advancements.