1:1 with Li Ding

Li Ding

Changing the Pre-silicon Design Robustness Analysis Game

We sat down with Li Ding, Synopsys Scientist in the Design Group, to learn more about Synopsys’ new PrimeShield statistical robustness analysis solution and what impact it will have on SoC design.

Q: Synopsys recently unveiled its new PrimeShield product. What is the significance of this product?

Li Ding:

Synopsys has been the leader in design analysis and signoff for many years, with PrimeTime static timing analysis recognized as the industry’s golden signoff solution. With rising manufacturing costs at advanced nodes, designers are introducing ever-increasing design margins to manage risk and ensure robustness. These margins are limiting semiconductor innovation, and are a significant challenge in designing the fastest, most efficient SoCs. PrimeShield, with its new statistical-based analysis engine, can provide guidance on ROI (return-on-investment) and optimization that improves design robustness while achieving the best power, performance, and area of the designs.

Q: What makes PrimeShield different from earlier statistical robustness analysis solutions?

Li Ding:

Speed and capacity, with the same golden accuracy, is the key differentiation versus other statistical analysis solutions. Statistical robustness analysis is a concept that has existed for decades but was never feasible for deployment beyond a small design block—even less so in a large-scale SoC production flow. With machine learning technology, PrimeShield can complete the same statistical analysis in minutes on a single execution host that previously could take days, and with the same accuracy.

Q: You emphasized PrimeShield is applicable to the industry’s largest SoCs, why is that important?

Li Ding:

PrimeShield leverages the golden signoff PrimeTime engine that can perform SPICE-accurate timing calculations and variation modeling in seconds. However, Monte Carlo simulation for true-statistical analysis still needs to repeat the same analysis thousands, or even millions, of times for high-sigma accuracy, which is much too time-consuming to keep up with the fast pace during SoC design implementation and signoff.

PrimeShield deploys machine learning in Monte Carlo simulation, which, because of its repetitive nature, is an excellent application for machine prediction, and speeds things up by 100X to 10,000X (yes, that’s ten thousand times!), enabling full-chip SoC or high-Sigma analysis that was never feasible before.

Q: You just mentioned high-Sigma analysis. What types of designs have such high levels of robustness requirements, and what were some of the challenges in doing that?

Li Ding:

High-reliability applications, like automotive designs, or small design circuits that are repeated thousands of times in an SoC, like artificial intelligence processing units, require high-Sigma robustness on very targeted circuitry. High-Sigma analysis requires millions of Monte Carlo samples and can easily take weeks of computation using thousands of cores. A lot of designers choose to extrapolate existing variation models or use FastSPICE solutions, but those flows cannot provide the same level of accuracy.

PrimeShield uses the machine learning technology I mentioned before and strategically utilizes Synopsys HSPICE through native links to achieve HSPICE Monte Carlo accuracy with feasible runtimes, typically within an hour on just 4 or 8 CPU cores.

Q: What about over-designing in today’s digital design flow without true statistical guidance? What guidance can designers expect from PrimeShield?

Li Ding:

Without true statistical guidance, designers are adding design margins to every part of the design, safeguarding themselves from statistical effects. PrimeShield can identify the statistical hotspots and a sorted list of high-ROI optimization targets for design margining and/or sensitivity reduction. By addressing these strategic targets, designers can reduce statistical loss and improve design robustness without unnecessary impact to design performance and efficiency.

Q: Could you elaborate more on the concept of design robustness and what metrics are measured?

Li Ding:

Design robustness is the concept of improving design immunity from potential changes in process, voltage, temperature, etc. Take voltage variation, for example. Typical voltage-drop analyses are vector-driven, which simulates a known event. However, an SoC chip may encounter many different scenarios throughout its operation cycles. Evaluating robustness and improving immunity to the unknown is also a key consideration for SoC designers.

Q: How does PrimeShield fit into the Synopsys Digital Design Family?

Li Ding:

Today, statistical timing analysis is typically done late in the design phase, close to design signoff, due to the cost and complexity of the analysis. PrimeShield’s fast and high-capacity robustness analysis engine means designers can finally optimize towards the best design PPA-R (performance, power, area, and robustness) throughout the design flow. PrimeShield is available within the Synopsys Digital Design Family and delivers designs with the best design robustness against variation. 

Q: Thank you for sharing this in-depth look at PrimeShield with us!

Li Ding:

You're welcome. PrimeShield is an exciting new product and we look forward to helping designers minimize design time and achieve best results in production.