DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Rasha Karakchi Advisor : Dr. Bakos Date : Nov 25th, 2019 Time : 9:30 am Place : Meeting Room 2265, Innovation Center Abstract Deterministic and Non-deterministic Finite Automata (DFA and NFA) comprise the fundamental unit of work for many emerging big data applications, motivating recent efforts to develop Domain-Specific Architectures (DSAs) to exploit fine-grain parallelism available in automata workloads. This dissertation presents NAPOLY (Non-Deterministic Automata Processor OverLaY), an overlay architecture and associated software that attempt to maximally exploit on-chip memory parallelism for NFA evaluation. In order to avoid an upper bound in NFA size that commonly affects prior efforts, NAPOLY is optimized for runtime reconfiguration, allowing for full reconfiguration in 10s of microseconds. NAPOLY is also parameterizable, allowing for offline generation of repertoire of overlay configurations with various trade-offs between state capacity and transition capacity. In this dissertation, we evaluate NAPOLY on automata applications packaged in ANMLZoo benchmarks using our proposed state mapping heuristic and off-shelf SAT solver. We compare NAPOLY's performance against existing CPU and GPU implementations. The results show NAPOLY performs best for larger benchmarks with more active states and high report frequency. NAPOLY outperforms in 10 out of 12 benchmark suite to the best of state-of-the-art CPU and GPU implementations. To the best of our knowledge, this is the first example of a runtime-reprogrammable FPGA-based automata processor overlay.