A resource-efficient monitoring architecture for hardware accelerated self-adaptive online data stream compression

In this paper, a novel scalable and resource-efficient architecture capable of monitoring the compressibility of a data stream with various entropy encoding algorithms is proposed. The self-adaptive architecture determines the best compression technique among many techniques which may be selected to encode an online data stream. This information can be used to reconfigure an adaptive encoding architecture dynamically at runtime to provide a high compression ratio. We have compared two hardware architectures that model the same functionality but perform the processing of the input data differently. This paper contributes a resource-efficient self-adaptive way of selecting the best lossless data compression method in hardware, independent of the end application. The processing architecture which uses soft-core processors provides approximately 35% resource savings as compared to the hardware implementation of processing modules in VHDL. Our experimental results show that the overall compression achieved by using self-adaptive architectures is around 14% better than that provided by the best compression technique in a non-adaptive system.