Improving Runtime Efficiency in Emulation Models of Server IPs

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This project explores improving runtime efficiency in emulation models of server Intellectual Properties (IPs) for modern Very Large Scale Integration (VLSI) design. Emulation involves mimicking functionality and behavior of real-time hardware systems, operating significantly faster than simulation at clock frequencies of several GHz. This enables execution of extensive test cases in reduced time, uncovering deep corner-case bugs that simulation typically fails to detect. Since emulation utilizes the same Register Transfer Level (RTL) code deployed on actual silicon, comprehensive bug elimination becomes critical for server IP quality. The primary objective is improving runtime efficiency of emulation models by optimizing total time spent by software and hardware components during emulation runs to achieve better resource utilization. The secondary objective focuses on developing Python-based automation scripts to reduce manual effort during validation and improve overall workflow efficiency. The work addresses challenges posed by high costs and limited availability of emulation platforms through systematic optimization of testbench and software components in collaboration with Ultra eXtensible Interconnect Verification Intellectual Property teams. Through systematic optimization phases spanning multiple development cycles, progressive improvements were achieved using data-driven analysis and targeted enhancement strategies. A comprehensive five-component automation validation framework was developed encompassing hardware configuration parsing, memory region analysis, test configuration generation, workflow coordination, and parallel execution management. This approach resulted in a remarkable 41% reduction in runtime, substantially exceeding the initial target of 30% improvement, while establishing scalable automation methodologies that transform validation preparation from manual processes into systematic, repeatable workflows for server IP validation.

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