A research paper released recently describes ways generative AI can assist one of the most complex engineering efforts: designing semiconductors.
The work demonstrates how companies in highly specialised 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 specialised methods, software programs and computer languages.
A Broad Vision for LLMs
“I believe over time large language models will help all the processes, across the board,” said Mark Ren, an NVIDIA Research Director and lead author of the paper.
Bill Dally, NVIDIA’s Chief Scientist, announced the paper in a keynote at the International Conference on Computer-Aided Design, an annual gathering of hundreds of engineers working in the field called electronic design automation, or EDA.
“This effort marks an important first step in applying LLMs to the complex work of designing semiconductors,” said Dally at the event in San Francisco. “It shows how even highly specialised fields can use their internal data to train useful generative AI models.”
ChipNeMo Surfaces
The paper details how NVIDIA engineers created for their internal use a custom LLM, called ChipNeMo, trained on the company’s internal data to generate and optimise software and assist human designers.
Long term, engineers hope to apply generative AI to each stage of chip design, potentially reaping significant gains in overall productivity, said Ren, whose career spans more than 20 years in EDA.
After surveying NVIDIA engineers for possible use cases, the research team chose three to start: a chatbot, a code generator and an analysis tool.
Initial Use Cases
The latter—a tool that automates the time-consuming tasks of maintaining updated descriptions of known bugs—has been the most well-received so far.
A prototype chatbot that responds to questions about GPU architecture and design helped many engineers quickly find technical documents in early tests.
A code generator in development (demonstrated above) already creates snippets of about 10–20 lines of software in two specialised languages chip designers use. It will be integrated with existing tools, so engineers have a handy assistant for designs in progress.
Customising AI Models with NVIDIA NeMo
The paper mainly focuses on the team’s work gathering its design data and using it to create a specialised generative AI model, a process portable to any industry.
As its starting point, the team chose a foundation model and customised it with NVIDIA NeMo, a framework for building, customising and deploying generative AI models that’s included in the NVIDIA AI Enterprise software platform. The selected NeMo model sports 43 billion parameters, a measure of its capability to understand patterns. It was trained using more than a trillion tokens, the words and symbols in text and software.
The team then refined the model in two training rounds, the first using about 24 billion tokens worth of its internal design data and the second on a mix of about 130,000 conversation and design examples.
The work is among several examples of research and proofs of concept of generative AI in the semiconductor industry, just beginning to emerge from the lab.
Sharing Lessons Learned
One of the most important lessons Ren’s team learned is the value of customising an LLM.
On chip-design tasks, custom ChipNeMo models with as few as 13 billion parameters match or exceed performance of even much larger general-purpose LLMs like LLaMA2 with 70 billion parameters. In some use cases, ChipNeMo models were dramatically better.
Along the way, users need to exercise care in what data they collect and how they clean it for use in training, he added.
Finally, Ren advises users to stay abreast of the latest tools that can speed and simplify the work.
NVIDIA Research has hundreds of scientists and engineers worldwide focused on topics such as AI, computer graphics, computer vision, self-driving cars and robotics. Other recent projects in semiconductors include using AI to design smaller, faster circuits and to optimize placement of large blocks.
Enterprises looking to build their own custom LLMs can get started today using NeMo framework available from GitHub and NVIDIA NGC catalogue.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)