Software-hardware codesign for efficient in-memory regular pattern matching

Regular pattern matching is used in numerous application domains, including text processing, bioinformatics, and network security. Patterns are typically expressed with an extended syntax of regular expressions. This syntax includes the computationally challenging construct of bounded repetition or counting, which describes the repetition of a pattern a fixed number of times. We develop a specialized in-memory hardware architecture that integrates counter and bit vector modules into a state-of-the-art in-memory NFA accelerator. The design is inspired by the theoretical model of nondeterministic counter automata (NCA). A key feature of our approach is that we statically analyze regular expressions to determine bounds on the amount of memory needed for the occurrences of bounded repetition. The results of this analysis are used by a regex-to-hardware compiler in order to make an appropriate selection of counter or bit vector modules. We evaluate our hardware implementation using a simulator based on circuit parameters collected by SPICE simulation in TSMC 28nm CMOS process. We find that the use of counter and bit vector modules outperforms unfolding solutions by orders of magnitude. Experiments concerning realistic workloads show up to 76% energy reduction and 58% area reduction in comparison to CAMA, a recently proposed in-memory NFA accelerator.