Efficient reconfigurable embedded parsers

Authors:
Christos Pavlatos;Alexandros C. Dimopoulos;Andrew Koulouris;Theodore Andronikos;Ioannis Panagopoulos;George Papakonstantinou
Affiliations:
Iroon Polytechneiou, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece;Iroon Polytechneiou, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece;Iroon Polytechneiou, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece;Department of Informatics, Ionian University, Plateia Tsirigoti 7, Corfu 49100, Greece;Iroon Polytechneiou, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece;Iroon Polytechneiou, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15773, Athens, Greece
Venue:
Computer Languages, Systems and Structures
Year:
2009

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Abstract

This paper presents a highly efficient architecture for the hardware implementation of context-free grammar (CFG) parsers. Its efficiency stems from an innovative combinatorial circuit that implements the fundamental operation of Earley's parsing algorithm in time complexity O(log"2|G|), where |G| is the size of the CFG. Using this hardware architecture in a template form, we have developed an automated synthesis tool that, given the specification of an arbitrary CFG, generates the HDL (Hardware Design Language) synthesizable source code of the hardware parser for the given grammar. The generated source has been simulated for validation, synthesized and tested on a Xilinx FPGA (Field Programmable Gate Array) board. Our method increases the performance by a factor of one to two orders of magnitude, compared to previous hardware implementations, depending on the size of the grammar and the input string length. The speedup, compared to the pure software implementation, varies from two orders of magnitude for toy-scale grammars to six orders of magnitude for large real life grammars. This makes it particularly appealing for applications where (syntactic) pattern recognition response is a crucial aspect.