Minh Luu, Surya Jasper, Khoi Le, Evan Pan, Michael Quinn, Aakash Tyagi, Jiang Hu
VCDiag is a tool that uses VCD data to classify failing waveforms in RTL-level simulations, achieving over 94% accuracy in identifying likely failure modules and significantly reducing data size for analysis.
VCDiag is a new tool designed to help engineers quickly identify and fix errors in the design of electronic systems. Traditionally, finding and diagnosing these errors can take a lot of time and effort, as it involves manually reviewing specifications and analyzing complex waveform data. VCDiag uses machine learning to automate this process, making it faster and more efficient. In tests, it was able to accurately identify the most likely problem areas in a design over 94% of the time, and it reduces the amount of data engineers need to look at by over 120 times.
