Nvidia Reveals How AI Can Revolutionize Chip Design. Nvidia, a leading designer of computer chips in artificial intelligence (AI), has revealed research demonstrating how AI can improve chip design. The process of designing a chip involves placing tens of billions of tiny on-off switches called transistors on a piece of silicon to create functioning chips. The precise location of these transistors affects the chip’s cost, speed, and power consumption. Design engineers use complex software from companies such as Synopsys and Cadence Design Systems to optimize the placement of these transistors.
Advanced AI optimizes chip design:
Nvidia’s latest paper shows that a combination of AI techniques can be used to place large groups of transistors more efficiently. The paper builds on a 2021 paper from Google, which was controversial and improves on it by adding a second layer of AI to existing reinforcement learning techniques developed by researchers from the University of Texas.
Design innovation drives progress:
Nvidia chief scientist Bill Dally explained that the research was essential because chip manufacturing improvements are slowing, and per-transistor costs in new generations of chip manufacturing technology are higher than in previous generations. This contradicts the famous prediction by Intel co-founder Gordon Moore that chips would always get cheaper and faster. Dally said, “To continue to move forward and deliver more value to customers, we can’t get it from cheaper transistors. We have to get it by being more clever on the design.”
The application of AI to chip design has significant implications for the industry. AI can automate many aspects of the design process, allowing engineers to focus on other tasks, such as verifying the design’s correctness. Additionally, AI can optimize chip design for specific applications, resulting in more efficient and cost-effective solutions. As the demand for AI-powered chips continues to grow, the use of AI in chip design is likely to become more widespread.
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