In a research paper released today, Nvidia semiconductor engineers showcased how generative artificial intelligence (AI) can assist in the complex process of designing semiconductors.
The study demonstrated how specialized industries can leverage large language models (LLMs) trained on internal data to create assistants that enhance productivity.
The research, utilizing Nvidia NeMo, highlights the potential for customized AI models to provide a competitive edge in the semiconductor field.
Semiconductor design is a highly challenging endeavor, involving the meticulous construction of chips containing billions of transistors on 3D circuitry maps that are like city streets — but thinner than a human hair.
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It requires the coordination of multiple engineering teams over a span of years. Each team specializes in different aspects of chip design, employing specific methods, software programs, and computer languages.
Mark Ren, an Nvidia Research director, was the lead author of the paper.
“I believe over time large language models will help all the processes, across the board,” Ren said in a statement.
The paper was announced by Bill Dally, Nvidia’s chief scientist, during a keynote at the International Conference on Computer-Aided Design held in San Francisco.
“This effort marks an important first step in applying LLMs to the complex work of designing semiconductors,” said Dally, in a statement. “It shows how even highly specialized fields can use their internal data to train useful generative AI models.”
The research team at
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