A GPU implementation for LBG and SOM training |
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Authors: | Yi Xiao Chi Sing Leung Tze-Yui Ho Ping-Man Lam |
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Affiliation: | (1) Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong;; |
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Abstract: | Vector quantization (VQ) is an effective technique applicable in a wide range of areas, such as image compression and pattern
recognition. The most time-consuming procedure of VQ is codebook training, and two of the frequently used training algorithms
are LBG and self-organizing map (SOM). Nowadays, desktop computers are usually equipped with programmable graphics processing
units (GPUs), whose parallel data-processing ability is ideal for codebook training acceleration. Although there are some
GPU algorithms for LBG training, their implementations suffer from a large amount of data transfer between CPU and GPU and
a large number of rendering passes within a training iteration. This paper presents a novel GPU-based training implementation
for LBG and SOM training. More specifically, we utilize the random write ability of vertex shader to reduce the overheads
mentioned above. Our experimental results show that our approach can run four times faster than the previous approach. |
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