A hybrid CPU-FPGA system for high throughput (10Gb/s) streaming document classification.

Processing large volumes of information in real time requires large amounts of computational power, which consumes a significant amount of energy. With the rise in the amount of data produced, energy-efficient high-performance information processing systems are becoming a necessity. We present a hybrid CPU-FPGA system for high-throughput classification of streams of textual documents (e.g. emails or web pages). The current system parses the document stream using a multicore CPU and performs classification on the parsed stream using Field-Programmable Gate Arrays (FPGAs). As an example, we demonstrate a Naive Bayes classifier on the TREC Aquaint dataset. Our current solution can classify 10Gb/s internet traffic in real time. Our aim is to increase the throughput to 100Gb/s by incorporating the parser into the FPGA design.