Semiconductor design software corporations Synopsys presented its "silicon-to-systems" vision as its product development strategy for the age of artificial intelligence (AI). The aim is to respond to an environment where decisions made at the semiconductor design stage determine the performance and reliability of end products such as automobiles, medical devices, and industrial equipment.
Synopsys said on the 29th that, prompted by its acquisition of engineering simulation corporations Ansys, it will strengthen a design approach that connects semiconductor design, intellectual property (IP), electronic design automation (EDA), multiphysics simulation, and system simulation.
As AI spreads into physical products, product development methods are also changing. Cars are shifting from mechanical structures to computing platforms, and medical devices and industrial equipment are evolving into intelligent systems that process data and predict failures. Synopsys said product competitiveness comes not from individual components but from the capability to co-design silicon, software, the physical environment, and system behavior.
The key is to predict system-level outcomes in the early stages of development. As specialized computing, advanced packaging, and embedded AI spread, performance, power efficiency, thermal characteristics, safety, verification, and expense are increasingly decided not in the latter stages of design but in the early stages.
Accordingly, the "shift left" approach is also becoming important. Shift left is an approach that performs verification, testing, and problem resolution in the early stages rather than the latter stages of development to reduce error correction expense and development risk. That is because problems discovered after semiconductor tape-out or actual prototype fabrication can significantly affect schedules and expense.
Synopsys described this as a "redesign of engineering." It means moving away from developing hardware and software, electronics and physics, and devices and systems separately and transitioning to a co-design framework. The focus of product development is also shifting from simple operability to how products behave over long periods in real environments.
AI-based engineering workflows were presented as a means to boost design productivity. Reinforcement Learning, Generative AI, and agent-based workflows can be used for design space exploration, analyzing trade-offs between performance and expense, and automating repetitive tasks. Synopsys viewed AI not as replacing engineers but as helping reduce repetitive work so they can focus on system design and product differentiation.
Digital Twin was also cited as a pillar of the design framework. A Digital Twin is a technology that creates a virtual model of a product to predict real-world behavior. Synopsys said that feeding data collected from products in operation back into the design phase can improve product reliability and performance and be used in next-generation product development.
Synopsys forecast that, going forward, semiconductor corporations will design products from a system-level perspective, and system corporations will expand their design scope to silicon architecture. Silicon determines performance, power efficiency, and scalability, while software governs adaptability and user experience. Only by considering the physical environment and system integration together can complex AI products be developed reliably.
A Synopsys representative said, "Future competitiveness lies not only in speed of development but also in maintaining predictability while managing complexity," and added, "Corporations that succeed will be those that consolidate the entire product lifecycle—from architecture, design, and verification to manufacturing, deployment, and operational optimization."