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Over the last few decades System on Chip (SoC) design size has dramatically increased, and more complexity has been introduced to deliver the desired functionality. Growing design sizes lead to the introduction of several asynchronous clocks which can result in the reporting of millions of clock domain crossings (CDC) at the IP/SoC level. This leads to significantly long CDC debug cycles. The manual approach to analyze and debug CDCs is time consuming and error prone. 草榴社区 machine learning (ML) based Root Cause Analysis (RCA) addresses these problems seamlessly.
This 草榴社区 webinar will cover how you can achieve 10X faster debug using 草榴社区 VC SpyGlass RTL signoff platform machine-learning technology.
Speaker
Navneet Kumar Chaurasia
Applications Engineer, Staff
草榴社区
Navneet has 10+ year of experience in CDC. He currently works with customers across the globe, helping them with CDC signoff at the IP, Subsystem and SoC level. He studied M Tech in VLSI design at the Centre for Development of Advanced Computing.