Chan, W.-K.W.-K.ChanTseng, Y.-H.Y.-H.TsengLin, Y.-S.Y.-S.LinSHAO-YI CHIEN2018-09-102018-09-10201415206130http://www.scopus.com/inward/record.url?eid=2-s2.0-84920268953&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/387320To support distributed video content analysis for video sensors in machine-to-machine networks, a reconfigurable stream processor for distributed smart cameras is proposed in this paper. A coarse-grained reconfigurable image stream processing architecture (CRISPA) with heterogeneous stream processing (HSP) and subword-level parallelism (SLP) is proposed to accelerate various algorithms for computer vision applications of smart-cameras. Implementation results show that the proposed design outperforms existing vision processors in many aspects: the on-chip memory size, power efficiency and area efficiency are 18.2 to 87.4 times, 4.5 to 12.5 times, and 3.8 to 25.5 times better than the state-of-the-art chips. Moreover, the programmability of the proposed design makes it capable of supporting many high-level computer vision algorithms in high specification. © 2014 IEEE.[SDGs]SDG7Cameras; Computer hardware description languages; Efficiency; Integrated circuit design; Machine-to-machine communication; Reconfigurable architectures; Silicon compounds; Coarse-grained reconfigurable; Computer vision applications; Distributed Smart Cameras; High-level computer vision; Machine to machines; Stream processing; Video-content analysis; Vision processor; Computer visionconference paper10.1109/SiPS.2014.6986097