Hardware Implementation for Scalable Lookahead Regular Expression Detection

Abstract—Regular Expressions (RegExes) are widely used in various applications to identify strings of text. Their flexibility, however, increases the complexity of the detection system and often limits the detection speed as well as the total number of RegExes that can be detected using limited resources. The two classical detection methods, Deterministic Finite Automaton (DFA) and Non-Deterministic Finite Automaton (NFA), have the potential problems of prohibitively large memory requirements and a large number of concurrent operations, respectively. Although recent schemes addressing these problems to improve DFA and NFA are promising, they are inherently limited by their scalability, since they follow the state transition model in DFA and NFA, where the state transitions occur per each character of the input. We recently proposed a scalable RegEx detection system called Lookahead Finite Automata (LaFA) to solve these problems with three novel ideas: 1. Provide specialized and optimized detection modules to increase resource utilizations. 2. Systematically reordering the RegEx detection sequence to reduce number of concurrent operations. 3. Sharing states among automata for different RegExes to reduce resource requirements. In this paper, we propose an efficient hardware architecture and prototype design implementation based on LaFA. Our proof-of-concept prototype design is built on a fraction of a single commodity Field Programmable Gate Array (FPGA) chip and can accommodate up to twenty-five thousand (25k) RegExes. Using only 7 % of the logic area and 25 % of the memory on a Xilinx Virtex-4 FX100, the prototype design can achieve 2-Gbps (gigabits-per-second) detection throughput with only one detection engine. We estimate that 34-Gbps detection throughput can be achieved if the entire resources of a state-of-the-art FPGA chip are used to implement multiple detection engines. I