Accelerating the debugging of FV traces using K-means clustering techniques

As the size and the complexity of today's HW designs increase significantly, the debugging process becomes a real bottleneck in the function verification life cycle. A huge amount of debugging data is generated during HW design simulation, emulation and prototyping sessions. So any attempt to automate the diagnosis of the resulted data can be of great help to reduce the debugging time and increase the diagnosis accuracy. This paper proposes the utilization of machine learning techniques to automate the diagnosis of design trace history. k-means clustering technique is used to group the trace segments that own huge similarity and identify the ones that occur rarely during the design execution time. We demonstrate the application of the proposed framework in guiding the functional verification debugging effort using a group of industrial HW designs.