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A reconfigurable stream compression hardware based on static symbol-lookup table

机译:基于静态符号查找表的可重构流压缩硬件

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摘要

When we consider any applications that use large data continuously produced, it is necessary for the system developer to apply some fast method that migrates the data stream to the processors. Even if we consider the internal communications of a BigData processing system, applications that treat dataflow such as from a sensor system with tens of channels to a peripheral bus for interconnections among processing modules are currently facing a critical frequency problem to exchange data in the busses because the data size has become very large. One of the best solutions to improve the situation is to compress the exchanged data stream during the transfer in the interconnection among processing modules. However, the conventional compression mechanisms used by software solutions such as ZIP and LZW need to aggregate the compressed data and a table that includes the information for recovering the compressed data to the original one. This paper shows a novel compression mechanism based on the symbol pair matching that uses a coherent and static lookup table with a limited number of entries of the symbol pairs. Building a compression pipeline with multiple tables we can implement an effective data path of the stream-based compression with a reconfigurable and flexible compression ratio applying trained tables from the original data characteristics. This paper shows the algorithm design and an implementation example on an FPGA using the content addressable memory and reports the performance of the hardware.
机译:当我们考虑使用连续产生的大数据的任何应用程序时,系统开发人员有必要应用某种快速的方法将数据流迁移到处理器。即使我们考虑到BigData处理系统的内部通信,处理数据流的应用程序(例如,从具有数十个通道的传感器系统到外围总线,用于处理模块之间的互连)当前也面临着交换总线中数据的关键频率问题,因为数据大小变得非常大。改善情况的最佳解决方案之一是在处理模块之间的互连传输过程中压缩交换的数据流。但是,诸如ZIP和LZW之类的软件解决方案使用的常规压缩机制需要聚合压缩数据和一个表,该表包括用于将压缩数据恢复到原始数据的信息。本文展示了一种基于符号对匹配的新颖压缩机制,该机制使用具有有限数量的符号对条目的相干静态查找表。建立具有多个表的压缩流水线,我们可以使用基于原始数据特征的训练表,通过可重构和灵活的压缩比来实现基于流的压缩的有效数据路径。本文展示了使用内容可寻址存储器的FPGA上的算法设计和实现示例,并报告了硬件性能。

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