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  4. Lossless data compression for improving the performance of a GPU-based beamformer
 
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Lossless data compression for improving the performance of a GPU-based beamformer

Journal
Ultrasonic Imaging
Journal Volume
37
Journal Issue
2
Pages
135-151
Date Issued
2015
Author(s)
Lok, U.-W.
Fan, G.-W.
PAI-CHI LI  
DOI
10.1177/0161734614547280
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/484565
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924810754&doi=10.1177%2f0161734614547280&partnerID=40&md5=d08cab038dcfe8045ceed82ed2bf236f
Abstract
The powerful parallel computation ability of a graphics processing unit (GPU) makes it feasible to perform dynamic receive beamforming However, a real time GPU-based beamformer requires high data rate to transfer radio-frequency (RF) data from hardware to software memory, as well as from central processing unit (CPU) to GPU memory. There are data compression methods (e.g. Joint Photographic Experts Group (JPEG)) available for the hardware front end to reduce data size, alleviating the data transfer requirement of the hardware interface. Nevertheless, the required decoding time may even be larger than the transmission time of its original data, in turn degrading the overall performance of the GPU-based beamformer. This article proposes and implements a lossless compression-decompression algorithm, which enables in parallel compression and decompression of data. By this means, the data transfer requirement of hardware interface and the transmission time of CPU to GPU data transfers are reduced, without sacrificing image quality. In simulation results, the compression ratio reached around 1.7. The encoder design of our lossless compression approach requires low hardware resources and reasonable latency in a field programmable gate array. In addition, the transmission time of transferring data from CPU to GPU with the parallel decoding process improved by threefold, as compared with transferring original uncompressed data. These results show that our proposed lossless compression plus parallel decoder approach not only mitigate the transmission bandwidth requirement to transfer data from hardware front end to software system but also reduce the transmission time for CPU to GPU data transfer. © The Author(s) 2014.
Subjects
beamformer; compression; GPU parallel programming; parallel decoder
SDGs

[SDGs]SDG7

Other Subjects
Bandwidth compression; Beamforming; Compaction; Computer graphics; Computer graphics equipment; Data compression; Data transfer; Decoding; Field programmable gate arrays (FPGA); Graphics processing unit; Parallel programming; Program processors; Transmissions; Beam formers; Decompression algorithm; Joint photographic experts group; Lossless data compression; Parallel Computation; Parallel decoder; Radio-frequency datum; Transmission bandwidth; Data reduction; algorithm; echography; image processing; imaging phantom; information processing; procedures; signal processing; Algorithms; Data Compression; Image Processing, Computer-Assisted; Phantoms, Imaging; Signal Processing, Computer-Assisted; Ultrasonography
Type
journal article

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