Automata Processing in Reconfigurable Architectures

Abstract

We present a general automata processing framework on FPGAs, which generates an RTL kernel for automata processing together with an AXI and PCIe based I/O circuitry. We implement the framework on both local nodes and cloud platforms (Amazon AWS and Nimbix) with novel features. A full performance comparison of the proposed framework is conducted against state-of-the-art automata processing engines on CPUs, GPUs, and Micron’s Automata Processor using the ANMLZoo benchmark suite and some real-world datasets. Results show that FPGAs enable extremely high-throughput automata processing compared to von Neumann architectures. We also collect the resource utilization and power consumption on the two cloud platforms, and find that the I/O circuitry consumes most of the hardware resources and power. Furthermore, we propose a fast, symbol-only reconfiguration mechanism based on the framework for large pattern sets that cannot fit on a single device and need to be partitioned. The proposed method supports multiple passes of the input stream and reduces the re-compilation cost from hours to seconds.