Learning microarchitectural behaviors to improve stimuli generation quality

Microarchitectural information regarding various aspects of instruction execution can help processor-level stimuli generators more easily reach verification goals. While many such aspects are based on common microarchitectural concepts, their specific manifestations are highly design-specific. We propose using an automatic method for acquiring such microarchitectural knowledge and integrating it into the stimuli generator. We start by extracting microarchitectural data from simulation traces. This data is fed to a decision tree learning algorithm that produces rules for microarchi-tectural behavior of instructions; these rules are then integrated into the testing knowledge of the stimuli generator. This testing knowledge can provide users with the ability to better control the microarchitectural behavior of generated instructions, leading to higher quality test cases. Experimental results on the POWER7 processor showed that our proposed method can improve the microarchitectural cover-age of the design