Achieving predictable chip design closure is becoming increasingly difficult for teams around the world. Linting tools have been around for decades, but traditional tools require significant effort to filter out noise and ultimately zero real design problems. As application-specific integrated circuits (ASICs) grow in size and complexity, chip designers need greater debug efficiency in managing large numbers of violations to ultimately reduce turnaround time (TAT). need.
The first two parts of this lint series establish how lint provides a comprehensive mechanism for checking basic chip design faults, and provide a guiding methodology for deeper functional lint analysis. We’ve touched on the many benefits of having one.
Recognizing differences between in-house coding styles and extensive experience working with industry leaders gives us unprecedented advantages to accelerate RTL and system-on-chip (SoC) design flows for our customers. I was. Solutions such as Synopsys VC SpyGlass™ CDC have already shown how advanced machine learning (ML) algorithms are valuable for achieving signoff for his SoC designs with scalable performance and high debug productivity. Prove it. Synopsys VC SpyGlass Lint’s latest offering leverages industry-standard practices and decades of expertise to include powerful ML capabilities that significantly improve designers’ debug efficiency.
In the final installment of this blog series, we cover the shortcomings of traditional linting tools, how ML features and root cause analysis (ML-RCA) can accelerate design signoff, the key advantages of Synopsys VC SpyGlass Lint, and smart linting. See the future of Ting.
Click here to read more…
