Title :
Accelerating Video-Mining Applications Using Many Small, General-Purpose Cores
Author :
Li, Eric ; Li, Wenlong ; Tong, Xiaofeng ; Li, Jianguo ; Chen, Yurong ; Wang, Tao ; Wang, Patricia P. ; Hu, Wei ; Du, Yangzhou ; Zhang, Yimin ; Chen, Yen-Kuang
Author_Institution :
Intel Corp., Santa Clara, CA
Abstract :
Emerging video-mining applications such as image and video retrieval and indexing will require real-time processing capabilities. A many-core architecture with 64 small, in-order, general-purpose cores as the accelerator can help meet the necessary performance goals and requirements. The key video-mining modules can achieve parallel speedups of 19times to 62times from 64 cores and get an extra 2.3times speedup from 128-bit SIMD vectorization on the proposed architecture.
Keywords :
data mining; parallel architectures; video retrieval; SIMD vectorization; accelerator; image indexing; image retrieval; many-core architecture; real-time processing; video indexing; video retrieval; video-mining; Acceleration; Bandwidth; Data mining; Image retrieval; Image segmentation; Indexing; Information retrieval; Internet; Surveillance; Yarn;
Journal_Title :
Micro, IEEE
