DocumentCode :
262114
Title :
7.3 A 1000fps vision chip based on a dynamically reconfigurable hybrid architecture comprising a PE array and self-organizing map neural network
Author :
Cong Shi ; Jie Yang ; Ye Han ; Zhongxiang Cao ; Qi Qin ; Liyuan Liu ; Nan-Jian Wu ; Zhihua Wang
Author_Institution :
Chinese Acad. of Sci., Beijing, China
fYear :
2014
fDate :
9-13 Feb. 2014
Firstpage :
128
Lastpage :
129
Abstract :
A vision chip is a high-speed and compact vision system that integrates an image sensor and parallel image processors on a single silicon die. Nowadays, high-speed vision chips with powerful recognition capabilities are greatly demanded in applications such as: industrial automation, security, entertainment, robotic vision, and human-machine interaction. Some 100-to-1,000fps vision chips have been reported [1-4]. These chips integrate pixel-parallel and row-parallel SIMD array processors to speed up low- and mid-level image processing [1,2]. Recently, microprocessors (MPU) have been embedded to carry out high-level image processing [3,4]. Although excellent in low- and mid-level processing, these systems are poor in high-level feature vector (FV) recognition tasks due to the von Neumann bottleneck of the MPU. As a consequence, these chips can no longer achieve 1,000fps system-level performance, from image acquisition to high-level feature-recognition processing.
Keywords :
high-speed techniques; image processing; image sensors; neural nets; parallel processing; PE array; dynamically reconfigurable hybrid architecture; high-speed compact vision system; image sensor; parallel image processors; pixel-parallel processors; row-parallel SIMD array processors; self-organizing map neural network; single silicon die; vision chip; Arrays; Face recognition; Image processing; Neural networks; Neurons; Program processors; Solid state circuits;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2014 IEEE International
Conference_Location :
San Francisco, CA
ISSN :
0193-6530
Print_ISBN :
978-1-4799-0918-6
Type :
conf
DOI :
10.1109/ISSCC.2014.6757367
Filename :
6757367
Link To Document :
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