What you’ll learn:
- How AI is driving chip design.
- How AI-powered EDA is being used in automation.
Artificial intelligence is fueling innovation across industries, driving demand in the semiconductor industry for more chips with exponentially more performance and energy efficiency. Not surprisingly, the chip industry has turned to AI to help meet these needs. By leveraging AI to automate tedious tasks throughout the chip design and development flow, as well as enhance human creativity and decision-making, AI is now fueling new chip design innovations that would have been unimaginable just a few years ago.
In this article, we’ll explore how chip designers are leveraging AI in innovative ways. These involve the acceleration of chip development despite growing complexity, designing for specific use cases like high-performance computing and automotive, and addressing the growing semiconductor engineering workforce gap.
Speeding Up Semiconductor Development with AI-Enabled Design
AI-enabled automation has been infused across the entire EDA stack to automate processes and enable step-function gains in development cycles through improved engineering productivity and chip quality. AI improves the design process by automating tedious and repetitive tasks and providing valuable insights early on and throughout, helping accelerate time to results and quality of results.
AI tools are particularly effective in highly repetitive domains, such as identifying errors and finding patterns in large amounts of data, and in areas where complex search spaces and choices go beyond the analytical capabilities of a human, such as optimizing power, performance, and area (PPA).
AI-driven tools can identify errors and suggest improvements that human designers might miss, which is crucial in a fast-paced industry where precision and speed are essential. Moreover, by implementing reinforcement learning, these tools improve with each iteration, enhancing their algorithms and compounding their impact with more projects through continuous learning.
Today, AI is utilized throughout the full EDA stack, including improving analog design and automating and enhancing verification and test processes. Embedded data-analytics capabilities aggregate and utilize the rich data generated across AI-enabled IC design, test, and manufacturing flows to help drive more intelligent decision-making and further improve chip quality, yield, and throughput.
Leveraging AI for Trillion-Transistor Systems
Advanced silicon requirements in segments like high-performance computing and automotive are shepherding the evolution to trillion-transistor systems comprised of modular, chiplet-based designs, which traditional design methods aren’t equipped to handle. Here, AI-enabled 3D design capabilities are available to analyze key factors like power distribution and interconnect planning for accelerators, ensuring the final design meets the specific needs of the application.
In addition, semiconductor IP plays a key role in the advancement of chip design architectures—it enables high interconnect speeds and secure, accurate and fast transfer of data on chips and between SoCs. Using silicon-proven, interoperable, and standards-compliant IP speeds chip development by reducing integration risk for manufacturing. It also helps ensure the resulting chips work as planned and don’t create data bottlenecks.
As the industry shifts to more modular, chiplet-based designs, AI-driven 3D design optimization and standards-compliant IP are increasingly mission-critical for silicon success.
Mitigating the Semiconductor Engineering Workforce Gap Using AI
At the heart of semiconductor innovation is a highly technical and skilled workforce. As the industry rapidly approaches an expected $1T by the end of the decade, so do concerns about scaling the global engineering workforce to meet these demands and new opportunities.
A looming semiconductor talent shortage threatens technology innovation across all industries, with more than one million additional semiconductor-trained workers needed by 2030 according to some projections (Deloitte, 2023). In this context, AI also emerges as a vital solution.
Recent advances in LLM-based generative AI for chip design feature collaborative capabilities. For example, expert tool guidance and collateral generation assist engineers throughout the entire EDA flow from digital, analog, and 3D design to verification and test. Using a 24/7 “copilot” assistant is a game-changer for chip designers, allowing them to scale their productivity, advance their skills, and receive critical support for questions and issues right when they need it.
Remaking the Semiconductor Industry with AI
AI-driven design is transforming the semiconductor industry by automating processes, improving efficiency, and fueling ingenuity to solve increasingly complex challenges. And the pace of innovation shows no sign of stopping. If we can grow our talented workforce, there’s no telling how empowered design teams will push the boundaries of what we think is possible by utilizing AI.
