{"id":144599,"date":"2023-08-09T15:21:21","date_gmt":"2023-08-09T06:21:21","guid":{"rendered":"http:\/\/ee.presscat.kr\/?post_type=research-achieve&p=144599"},"modified":"2026-04-13T14:50:49","modified_gmt":"2026-04-13T05:50:49","slug":"ee-professor-kim-joo-young-developed-a-chatgpt-core-ai-semiconductor-with-a-2-4-fold-improvement-in-price-efficiency","status":"publish","type":"research-achieve","link":"http:\/\/ee.presscat.kr\/en\/research-achieve\/ee-professor-kim-joo-young-developed-a-chatgpt-core-ai-semiconductor-with-a-2-4-fold-improvement-in-price-efficiency\/","title":{"rendered":"EE Professor Kim Joo-Young Developed A ChatGPT Core AI Semiconductor with A 2.4-fold Improvement in Price efficiency"},"content":{"rendered":"

EE Professor Kim Joo-Young Developed A ChatGPT Core AI Semiconductor with A 2.4-fold Improvement in Price efficiency<\/span><\/strong><\/span><\/p>\n

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The ChatGPT released by OpenAI has captured global attention, and everyone is closely observing the changes this technology will bring out. <\/span><\/p>\n

This technology is based on large language models (LLM), which represent an unprecedented scale of artificial intelligence (AI) models compared to conventional AI. <\/span><\/p>\n

However, the operation of these models requires a significant number of high-performance GPUs, leading to astronomical computing costs. \u00a0<\/span><\/p>\n

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KAIST (President: Lee Kwang-Hyung) announced that research team led by EE Professor Kim Joo-Young Kim has successfully developed an AI semiconductor that efficiently accelerates the inference operations of large language models, which play a crucial role in ChatGPT.\u00a0<\/span><\/p>\n

The developed AI semiconductor, named the ‘Latency Processing Unit (LPU),’ efficiently accelerates the inference operations of large language models. It incorporates a high-speed computing engine capable of maximizing memory bandwidth utilization and performing all necessary inference computations rapidly.<\/span><\/p>\n

Additionally, it comes equipped built-in networking capabilities, making it easily expandable with multiple accelerators.\u00a0This LPU-based acceleration appliance server achieved up to a 50% higher performance and approximately 2.4 times better performance-to-price ratio compared to a supercomputer based on the industry-leading high-performance GPU, NVIDIA A100.<\/span><\/p>\n

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This advancement holds the potential to replace high-performance GPUs in data centers that are experiencing a rapid surge in demand for generative AI services.\u00a0This research was conducted by Professor HyperExcel Co., founded by Professor Kim Joo-Young and achieved the remarkable accomplishment of receiving the “Engineering Best Presentation Award” at the International Design Automation Conference (DAC 2023) held in San Francisco on July 12th (U.S. time).<\/span><\/p>\n

DAC is a prestigious international conference in the field of semiconductor design, particularly showcasing global semiconductor design technologies related to Electronic Design Automation (EDA) and Semiconductor Intellectual Property (IP).<\/span><\/p>\n

DAC attracts participation from renowned semiconductor design companies such as Intel, NVIDIA, AMD, Google, Microsoft, Samsung, TSMC, as well as top universities including Harvard, MIT, and Stanford.<\/span><\/p>\n

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Among the world\u2019s notable semiconductor technologies, Professor Kim’s team stands out as the sole recipient of an award for AI semiconductor technology tailored for large language models.<\/span><\/p>\n

This award acknowledges their AI semiconductor solution as a groundbreaking means to drastically reduce the substantial costs associated with inference operations for large language models on the global stage.<\/span><\/p>\n

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Professor Kim stated, “With the new processor ‘LPU’ for future large AI computations, I intend to pioneer the global market and take a lead over big tech companies in terms of technological prowess.”<\/span><\/p>\n

(Note: The provided translation is an elaboration and summary of the original text for clarity and readability.)<\/span><\/p>\n

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