A high performance architecture for computing burrows-wheeler transform on FPGAs

Cheema, Umer I.; Khokhar, Ashfaq

doi:10.1109/reconfig.2013.6732262

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…Parallel algorithms and the hardware design of BWT are also studied to improve the speed, including the parallel architecture [3,15] and the practical hardware acceleration, for example, FPGA (field-programmable gate array) [16] and GPU (graphic processing unit) [17]. The advancement is that parallel algorithms benefit the hardware BWT performance, and the researchers tend to simplify the hardware design so that they can obtain higher speeds [3,17], but the algorithms such as SA-IS are still complex for the sensors.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…As shown in the first figure of [16], the principle of BWT is sorting the data to fit the compression. Sorting is the primary computation in BWT, which determines the performance.…”

Section: Cz-bwt Encoding and Decodingmentioning

confidence: 99%

“…Sorting is the primary computation in BWT, which determines the performance. We use the same example as that in [16] to explain the truncated string sorting in CZ-BWT. Figure 3 shows the matrices for BWT sorting.…”

Section: Cz-bwt Encoding and Decodingmentioning

confidence: 99%

“…As shown in Figure 7(c), this phase is the 16b integer bucket sorting. The bucket array has 2 16 counters, so that we can scan Column [0,7] once and write the sorted data to Column [7,6]. Because the amount of the 16b integers is related to the previous 8b integers, Column 7 will be the same as that in phase 1.…”

Section: Cz-bwt Decodingmentioning

confidence: 99%

“…The figures in [16] show that the truncated BWT fits the hardware design, but it uses merge sort, which has the time complexity O(N 2 lbN). CZ-BWT uses bucket sort, which is both fast and easy for the hardware design.…”

Section: Complexities Of Hardware Designmentioning

confidence: 99%

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Fast Algorithm of Truncated Burrows-Wheeler Transform Coding for Data Compression of Sensors

Qin

Zhong

2018

Journal of Sensors

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Lots of sensors in the IoT (Internet of things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. So the study of big data compression is very useful in the field of sensors. In practice, BWT (Burrows-Wheeler transform) can gain good compression results for some kinds of data, but the traditional BWT algorithms are neither concise nor fast enough for the hardware of sensors, which will limit the BWT block size in a very small and incompetent scale. To solve this problem, this paper presents a fast algorithm of truncated BWT named "CZ-BWT algorithm" and implements it in the shareware named "ComZip." CZ-BWT supports the BWT block up to 2 GB (or larger) and uses the bucket sort. It is very fast with the time complexity O(N) and fits the big data compression. The experiment results indicate that ComZip with the CZ-BWT filter is obviously faster than bzip2, and it can obtain better compression ratio than bzip2 and p7zip in some conditions. In addition, CZ-BWT is more concise than current BWT with SA (suffix array) sorts and fits the hardware BWT implementation of sensors.

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Section: Journal Of Sensorsmentioning

confidence: 99%

“…As shown in the first figure of [16], the principle of BWT is sorting the data to fit the compression. Sorting is the primary computation in BWT, which determines the performance.…”

Section: Cz-bwt Encoding and Decodingmentioning

confidence: 99%

Section: Cz-bwt Encoding and Decodingmentioning

confidence: 99%

Section: Cz-bwt Decodingmentioning

confidence: 99%

Section: Complexities Of Hardware Designmentioning

confidence: 99%

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Fast Algorithm of Truncated Burrows-Wheeler Transform Coding for Data Compression of Sensors

Qin

Zhong

2018

Journal of Sensors

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Pipelined decoder for the limited context order Burrows–Wheeler transformation

Safieh

Freudenberger

2018

IET Circuits, Devices & Systems

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The Burrows-Wheeler transformation (BWT) is a reversible block sorting transform that is an integral part of many data compression algorithms. This work proposes a memory-efficient pipelined decoder for the BWT. In particular, the authors consider the limited context order BWT that has low memory requirements and enable fast encoding. However, the decoding of the limited context order BWT is typically much slower than the encoding. The proposed decoder pipeline provides a fast inverse BWT by splitting the decoding into several processing stages which are executed in parallel.

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