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For years, EDA companies have claimedartificial intelligence features in their IC design tools. In the past year, however,generative AI has undergone adramatic evolutionwith platforms like ChatGPT, causing some designers to question whether previous EDA features still count as AI by today's standards.

Synopsys aims to keep pace with this accelerating field by unveilinga new extension to its Synopsys.ai EDA suite. This announcement follows the release ofGoogles DeepMind, which uses AI to accelerate its in-house chip designs. Both of these announcements indicate how advanced machine learning algorithms are shaping IC development and how they might be used as a tool for designers in such fields.

Synopsys describes itsnew extension as an AI-driven analytics tool designed to span the entire integrated circuit development process, from initial design to manufacturing and testing. To this end, the Synopsys EDA Data Analytics solution offers several features that set it apart.

First, it provides comprehensive data aggregation capabilities, pulling in data from various stages of IC design, testing, and manufacturing. This gives designersa holistic view of the entire chip development lifecycle. The tool incorporates intelligence-guided debugging and optimization, which not only speeds up design closure but also minimizes project risks. This is particularly crucial in an industry where time to market can be a make-or-break factor.

Another standout feature of the extension is its focus on fabrication yield. This tool is designed to improve fab yield for faster ramp-up and more efficient high-volume manufacturing. Additionally, the tool can uncover silicon data outliers across the semiconductor supply chain, thereby improving chip quality, yield, and throughput.

Synopsys says the new tools can alsouncover new opportunities in power, performance, and area (PPA). By leveraging advanced AI algorithms, the tool can analyze magnitudes of heterogeneous, multi-domain data to accelerate root-cause analysis.

The news from Synopsys comes on the heels of a similar announcement from Google's parent company, Alphabet.

Recently, the group announced that it would be leveraging Google's DeepMind for AI-assisted chip design for use in its data centers. DeepMind usesa concept known as circuit neural networks to treata circuit as if it were a neural network, turning edges into wires and nodes into logic gates.

Then, using classical AI techniques like simulated annealing, DeepMind searches for the most efficient configurations, looking many steps into the future to improve circuit design. Utilizing advanced AI models like AlphaZero and MuZero, which are based on reinforcement learning, DeepMind has achieved "superhuman performance" in various circuit-design tasks.

While both Synopsys and Google's DeepMind are leveraging artificial intelligence to revolutionize chip design, their approaches and focus areas are distinct.

Synopsys' newly announced solution is part of its broader Synopsys.ai EDA suite, which aims to provide designers with an end-to-end, comprehensive toolset for the entire IC chip development lifecycle. These tools aggregate and analyze data across multiple domains to enable intelligent decision-making, speed up design closure, and improve fabrication yield.

DeepMind, on the other hand, takes a more specializedapproach. It employs advanced AI models to tackle specific optimization problems within chip design. While highly effective, this approach is more narrow in scope, focusing on individual aspects of the chip design process rather than offering a comprehensive, full-stack solution. Unlike Synopsys tool, DeepMinds AI is only for the internal optimization of Googles hardware.

Featured image (modified) used courtesy of Synopsys.

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From Synopsys to Google, New EDA Tools Apply Advanced AI to IC ... - All About Circuits