FlexAmata: A Universal and Efficient Adaption of Applications to Spatial Automata Processing Accelerators
Elaheh Sadredini, Reza Rahimi, Marzieh Lenjani, Mircea R. Stan, Kevin Skadron · 2020
Pattern matching, especially for complex patterns with many variations, is an important task in many big-data applications and maps well to finite automata. Recently, a variety of research has focused on hardware acceleration of automata processing, especially via spatial architectures that directly map the patterns to massively parallel hardware elements, such as in FPGAs and in-memory solutions. We observed that all existing automata-acceleration architectures are designed based on fixed, 8-bit symbol processing, derived from ASCII processing. However, the alphabet size in pattern-matching applications varies from just a few up to billions of unique symbols. This makes it difficult to provide a universal and efficient mapping of this wide variety of automata applications to existing automata accelerators.