Synopsys continuously updates its AI-driven EDA solutions to further optimize processes for complex, evolving technologies such as chiplets, 2.5D advanced packages, and 3D stacked die. As Ghazi confirmed, Synopsys engineers are currently evaluating next-generation AI applications such as ChatGPT for EDA. Developed by OpenAI, ChatGPT harnesses reinforcement learning from human feedback (RLHF) to learn from mistakes, challenge incorrect premises, and accurately answer follow-up questions.
Although Synopsys.ai does not use the generative AI technology upon which ChatGPT is based, engineering teams are exploring how to best leverage a new generation of multimodal large language models (LLMs) to streamline internal processes and augment existing solutions.
"One potential use we see for these AI applications—including ChatGPT—is further simplifying ease of use and enabling customers to seamlessly navigate a full-stack design, testing, and verification process," stated Ghazi.
To be sure, design requirements in the SysMoore era are increasingly demanding with multiple technologies converging in unified packages to address growing systemic and scale complexities. As Moore's law blends with new innovations that address these complexities, independently analyzing individual components is no longer practical. Rather, engineers require hyper-convergent design flows to deliver comprehensive, simplified analysis of entire systems, including the multi-die architectures that are enabling them to go beyond Moore's law.
Synopsys.ai minimizes design complexity by holistically optimizing PPA and fully automating repetitive tasks such as design space exploration, verification coverage, regression analytics, and test program generation. Centralizing and simplifying design flows empower engineers to focus on differentiation and migrate even the most advanced chiplet designs from foundry to foundry or from process node to process node.
“Synopsys is at the forefront of making multi-die design, verification, and testing efficient and cost-effective at every stage,” added Ghazi. “These next-generation chiplets will power a wide range of use cases, including AI and ML applications, advanced driver-assistance systems (ADAS), and high-performance computing (HPC) in tomorrow's data centers."