scholarly journals Simultaneous Finite Automata: An Efficient Data-Parallel Model for Regular Expression Matching

2013 ◽  
Author(s):  
Ryoma Sinya ◽  
Kiminori Matsuzaki ◽  
Masataka Sassa
Keyword(s):  
Regular Expression ◽  
Finite Automata ◽  
Parallel Model ◽  
Data Parallel ◽  
Regular Expression Matching ◽  
Efficient Data

scholarly journals Software Toolchain for Large-Scale RE-NFA Construction on FPGA

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Yi-Hua E. Yang ◽  
Viktor K. Prasanna
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Regular Expression ◽  
Finite Automata ◽  
Fixed Number ◽  
Regular Expressions ◽  
Pattern Complexity ◽  
Regular Expression Matching ◽  
Area Increase ◽  
Prototype Software

We present a software toolchain for constructing large-scaleregular expression matching(REM) on FPGA. The software automates the conversion of regular expressions into compact and high-performance nondeterministic finite automata (RE-NFA). Each RE-NFA is described as an RTL regular expression matching engine (REME) in VHDL for FPGA implementation. Assuming a fixed number of fan-out transitions per state, ann-statem-bytes-per-cycle RE-NFA can be constructed inO(n×m)time andO(n×m)memory by our software. A large number of RE-NFAs are placed onto a two-dimensionalstaged pipeline, allowing scalability to thousands of RE-NFAs with linear area increase and little clock rate penalty due to scaling. On a PC with a 2 GHz Athlon64 processor and 2 GB memory, our prototype software constructs hundreds of RE-NFAs used by Snort in less than 10 seconds. We also designed a benchmark generator which can produce RE-NFAs with configurable pattern complexity parameters, including state count, state fan-in, loop-back and feed-forward distances. Several regular expressions with various complexities are used to test the performance of our RE-NFA construction software.

Hybrid Finite Automata-Based Algorithm for Large Scale Regular Expression Matching

2012 ◽  
Vol 263-266 ◽  
pp. 3108-3113
Author(s):  
Wei He ◽  
Yun Fei Guo ◽  
Hong Chao Hu
Keyword(s):  
Large Scale ◽  
Regular Expression ◽  
Finite Automata ◽  
System Throughput ◽  
Hybrid Automata ◽  
Deterministic Finite Automaton ◽  
State Explosion ◽  
Regular Expression Matching ◽  
High System ◽  
Memory Utilization

Fast data transmission put forward high requirements on network content matching (NCM). Due to the high time complexity, Nondeterministic Finite Automata (NFA) was unable to meet the demand of regular expression matching (REM) which was the core of NCM; Transfer NFA to Deterministic Finite Automaton (DFA) could enhance the throughput, but led to state explosion, which increased demand for memory. To balance memory and throughput, state explosion in the transformation from NFA to DFA has been analyzed and a new method DC-DFA is presented for large scale REM. DC-DFA is based on hybrid automata structure which composed of NFA and DFA. DC-DFA introduces GradeOne classification to cut the memory usage and deep classification to improve throughput. The results show that for serious state explosion, DC-DFA could reduce 75% DFA states and improve memory utilization efficiently while maintain high system throughput.

Design of A New Type of Regular Expression Matching Engine Based on FPGA

2021 ◽  
Author(s):  
Nan Jiang ◽  
Ping Lin ◽  
Yulong He ◽  
Zhuozhi Tan ◽  
Jin Hu
Keyword(s):  
Regular Expression ◽  
Regular Expression Matching ◽  
New Type
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