Automata processing in reconfigurable architectures: in-the-cloud deployment, cross-platform evaluation, and fast symbol-only reconfiguration

摘要

In this new world of heterogeneous computing, automata processing has seen renewed interest with the advent of new workloads like machine learning, gene sequence matching, and other established disciplines like compiler design. This paper makes a good case for state-of-the-art field-programmable gate arrays (FPGAs) to powerfully assist automata processing. Traditional sequential processors like central processing units (CPUs) and graphics processing units (GPUs) need to store states and transition rules in the memory. This can suffer from look-up latencies due to random-access memory. For large dataset input streams, this can be inefficient.