Reconfigurable computing is the application of adaptable fabrics to address computational problems, often taking advantage of the flexibility of field‐programmable gate arrays (FPGAs) to produce problem‐specific solutions. It has been successfully applied to fields as diverse as machine learning, digital signal processing, cryptography, bioinformatics, logic emulation, CAD tool acceleration, scientific computing, and rapid prototyping.In this article, intended for the nonspecialist, we describe some of the basic concepts, tools, and architectures associated with reconfigurable computing.
This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms. The TFIDF classifier is used to detect web attacks in HTTP data. In our parallel hardware approaches, we design streaming, real time classifiers by simplifying the sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. Parallel implementations on the Tilera 64-core System on Chip and the Xilinx Virtex 5-LX FPGA are presented. For the Tilera, we employ a reduced state machine to recognize dictionary terms without requiring explicit tokenization, and achieve throughput of 37 MB/s at a slightly reduced accuracy. For the FPGA, we have developed a set of software tools to help automate the process of converting training data to synthesizable hardware and to provide a means of trading off between accuracy and resource utilization. The Xilinx Virtex 5-LX implementation requires 0.2% of the memory used by the original algorithm. At 166 MB/s (80X the software) the hardware implementation is able to achieve Gigabit network throughput at the same accuracy as the original algorithm.
Reconfigurable computing is the application of adaptable fabrics to solve computational problems, often taking advantage the flexibility available in the fabric to produce problem‐specific architectures that achieve high performance because of customization. Reconfigurable computing has been successfully applied to fields as diverse as digital signal processing, cryptography, bioinformatics, logic emulation, CAD tool acceleration, scientific computing, and rapid prototyping. Although Estrin‐first proposed the idea of a reconfigurable system in the form of a fixed plus variable structure computer in 1960 it has only been in recent years that reconfigurable fabrics have reached sufficient density to make them a compelling implementation platform for high Performance applications and embedded systems. In this article, intended for the non‐specialist, we describe some of the basic concepts, tools and architectures associated with reconfigurable computing.
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