Despite the performance and power efficiency gains achieved by FPGAs for text analytics queries, analysis shows a low utilization of the custom hardware operator modules. Furthermore the long synthesis times limit the accelerator's use in enterprise systems to static queries. To overcome these limitations we propose the use of an overlay architecture to share area resources among multiple operators and reduce compilation times. In this paper we present a novel soft-core architecture tailored to efficiently perform relational operations of text analytics queries on multiple virtual streams. It combines the ability to perform efficient streaming based operations while adding the flexibility of an instruction programmable core. It is used as a processing element in an array of cores to execute large query graphs and has access to shared co-processors to perform string-and context-based operations. We evaluate the core architecture in terms of area and performance compared to the custom hardware modules, and show how a minimum number of cores can be calculated to avoid stalling the document processing.
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