Applying the power of AI to design chip goes mainstrem

Over the past few years, artificial intelligence (AI) has played an increasingly important role in the chip development process. Since its release, Synopsys Users Group (SNUG) has announced that customers have taped out more than 100 chips using #AI tools to increase productivity and produce significantly better chips. The company used Synopsys DSO.ai to achieve product differentiation and power, performance, and area (#PPA) enhancements.

This is where AI-driven automation can help address the growing need for differentiated products while freeing design teams to focus on the innovation pipeline (Synopsys' DSO.ai - Design Space Optimization AI - is a solution for AI-driven chip design ).?

Using reinforcement learning techniques to autonomously search the vast chip design space for optimal solutions, AI assistants can help engineers build better, faster, and cheaper semiconductors.

Now that AI has proven its worth in chip design—even for AI applications—where do we go from here?

At Synopsys Users Group (SNUG) , we see the clear benefits that expanding the use of AI in the chip design and verification process can bring to silicon outcomes and productivity. AI can not only solve the iterative work of chip design, but also speed up the process and help teams get to the finish line faster. By shortening design and verification cycles and reducing effort, design teams can spend more time on innovative core ideas, saving them a lot of time and money so they can focus on what they do best: innovating.

In short, artificial intelligence augments human capabilities. Humans are great at coming up with unique ideas, but those ideas are usually at the system or architectural level. To implement these ideas, engineers need #EDA tools. To help accelerate implementation, Synopsys sees AI as the way forward. By integrating AI into our solutions and using them as easily and seamlessly as possible, we help engineers achieve their goals faster.

AI has the huge potential of many aspects of improving chip development: logical design, implementation, verification, verification, testing, etc. We are at the beginning of the new era of AI-driven design. With the re-emergence of hardware demand in each market, fortunately, we have AI to help us accelerate the design of chip design on each node.