Machine Learning-Assisted VCD Processing for Accelerated Dynamic Voltage Drop Analysis

With escalating power integrity challenges in advanced technologies, acquiring accurate dynamic power supply noise through Dynamic Voltage Drop (DVD) analysis becomes increasingly demanding. As noise margins shrink, the use of Value Change Dump (VCD) files for precise DVD analysis is indispensable but computationally expensive. Furthermore, the substantial storage requirements of VCD files, which record digital waveforms from logical simulations, pose significant challenges. In this article, we propose a machine learning (ML)-assisted VCD processing framework to accelerate DVD analysis and improve data efficiency. Transitions recorded in VCD files are mapped to a Physical Design-Aware Circuit Hierarchy Tree (CHT) for efficient feature extraction. These features are leveraged by an XGBoost-based predictor to identify critical vector time windows within the VCD, significantly reducing simulation complexity. Additionally, Huffman encoding is applied to compress signal names, further optimizing storage utilization. Experimental results show that DVD analysis using our profiled VCD files achieves a speedup of approximately 3.53× with an error margin of only 3.89%.