Kubilay Atasu, Thomas Parnell, et al.
ICPP 2017
With the ever growing amount of unstructured data, high-speed content analysis becomes ever more important. Enabling efficient search functions to help locate specific and relevant information hidden in this big data is a crucial task of today's enterprise systems, and can lead to valuable insights. A key component of content analysis systems are text parsers, which transform unstructured text data into structured information. Cascaded grammars offer a popular and powerful representation of text parsers by enabling the definition of more complex patterns in terms of simpler ones in a hierarchical fashion. This work presents a compilation framework to generate an optimized FPGA pipeline from a cascaded grammar description. We also describe the system integration and the way FPGA-based accelerators can be used as part of larger analysis tasks within Unstructured Information Management Application (UIMA) pipelines. We compare the performance of the hardware-accelerated system and a commercial software implementation using real-life UIMA pipelines from the healthcare domain. We show that the FPGA-accelerated system processes the parsing stage of a UIMA pipeline up to 31 times faster than the software implementation running on a high-end server, which results in an acceleration of up to 5 times for the complete pipeline.
Kubilay Atasu, Thomas Parnell, et al.
ICPP 2017
Raphael Polig, Kubilay Atasu, et al.
FPL 2014
Jan Van Lunteren, Ronald Luijten, et al.
DATE 2019
Jonathan Rohrer, Kubilay Atasu, et al.
CODES+ISSS 2009