A parallelizable approach for mining likely invariants

A relevant aspect in design analysis and verification is monitoring how logic relations among different variables change at run time. Current static approaches suffer from scalability problems that prevent their adoption on large designs. On the contrary, dynamic techniques scale better from the memory-consumption point of view. However, to achieve a high accuracy, they require to analyse a huge number of (long) execution traces, which results in time-consuming phases. In this paper, we present a new efficient approach to automatically infer logic relations among the variables of a design implementation. Both a sequential and a GPU-oriented parallel implementation are proposed to dynamically extract likely invariants from execution traces on different time windows. Execution traces composed of millions of simulation instants can be efficiently analysed.