Lossless Data Compression for Improving the Performance of a GPU-Based Beamformer

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.

Minimizing Image Quality Loss After Channel Count Reduction for Plane Wave Ultrasound via Deep Learning Inference

“…Yet, this approach inherently makes the system less reconfigurable, so it is ill-suited for the implementation of new channel-domain imaging methods [2]. Perhaps a less restrictive data transfer reduction solution is to add a lossless compressiondecompression algorithm to the system's data streaming operations [26]. Nonetheless, the compression ratio that can be achieved tends to vary depending on the characteristics of the acquired channel RF samples, so it is uncertain as to whether the size of the compressed data streaming traffic can always be accommodated by the system.…”

Real-Time Lossless Compression Algorithm for Ultrasound Data Using BL Universal Code

“…Data compression has been investigated to reduce the amount of ultrasound data in software-based ultrasound imaging systems [ 10 , 11 , 12 ]. The MPEG (moving picture experts group) compression method was utilized to compress radio-frequency (RF) data.…”

“…However, it requires high computation and memory resources for coding and decoding [ 11 ]. More recently, a lossless compression method for improving the performance of GPU-based beamformers was proposed [ 12 ]. However, this method needs additional memory (e.g., address memory) for decoding, and hence the compressed data can be larger than the original data because of the additional addresses.…”

Real-Time Lossless Compression Algorithm for Ultrasound Data Using BL Universal Code

“…Data compression has been investigated to reduce the amount of ultrasound data in softwarebased ultrasound imaging systems [10][11][12]. The MPEG compression method was utilized to compress radio-frequency (RF) data.…”

“…However, it requires high computation and memory resources for coding and decoding [11]. More recently, a lossless compression method for improving the performance of GPU-based beamformers was proposed [12]. However, this method needs additional memory (e.g., address memory) for decoding, and hence the compressed data can be larger than the original data because of the additional addresses.…”