Using AI, electronic design automation tools can significantly enhance power efficiency, performance, and chip area—the holy grail of design automation. AI can automate various chip design tasks and the technology can also generate more optimal layouts for integrated circuits, reducing the chip designer’s time and effort required for this process.
A research paper released last month describes ways generative AI can assist one of the most complex engineering efforts: designing semiconductors. The work demonstrates how companies in highly specialized fields can train large language models (LLMs) on their internal data to build assistants that increase productivity.
Few pursuits are as challenging as semiconductor design. Under a microscope, a state-of-the-art chip like an NVIDIA H100 Tensor Core GPU (above) looks like a well-planned metropolis, built with tens of billions of transistors, connected on streets 10,000x thinner than a human hair. Multiple engineering teams coordinate for as long as two years to construct one of these digital megacities.
Some groups define the chip’s overall architecture, some craft and place a variety of ultra-small circuits, and others test their work. Each job requires specialized methods, software programs and computer languages.
SOURCE:
Silicon Volley: Designers Tap Generative AI for a Chip Assist
Semiconductor engineers show how a specialized industry can customize large language models to gain an edge using NVIDIA NeMo. https://blogs.nvidia.com/blog/llm-semiconductors-chip-nemo/
The author of Silicon Valley: Designers Tap Generative AI for a Chip Assist is a staff writer at NVIDIA. Previously, he worked as a reporter and editor at “EE Times” for 28 years and served as managing editor of “Asian Computer Monthly” based in Hong Kong. Writes about technology, business and engineering for a broad audience of practitioners, their vendors, and the customers they serve.
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More on AI and Chip Design:
AI Is Reshaping Chip Design. But Where Will It End? Revolutionizing Chip Design With AI In Race for AI Chips, Google DeepMind Uses AI to Design Specialized Semiconductors
