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Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems
Nov.-Dec. 2012 (vol. 32 no. 6)
pp. 38-50
ASCII Text
x 
 
Jinwook Oh, Gyeonghoon Kim, Injoon Hong, Junyoung Park, Seungjin Lee, Joo-Young Kim, Jeong-Ho Woo, Hoi-Jun Yoo, "Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems," IEEE Micro, vol. 32, no. 6, pp. 38-50, Nov.-Dec., 2012.
 
 
BibTex
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@article{ 10.1109/MM.2012.90,
author = {Jinwook Oh and Gyeonghoon Kim and Injoon Hong and Junyoung Park and Seungjin Lee and Joo-Young Kim and Jeong-Ho Woo and Hoi-Jun Yoo},
title = {Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems},
journal ={IEEE Micro},
volume = {32},
number = {6},
issn = {0272-1732},
year = {2012},
pages = {38-50},
doi = {http://doi.ieeecomputersociety.org/10.1109/MM.2012.90},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - MGZN
JO - IEEE Micro
TI - Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems
IS - 6
SN - 0272-1732
SP38
EP50
EPD - 38-50
A1 - Jinwook Oh,
A1 - Gyeonghoon Kim,
A1 - Injoon Hong,
A1 - Junyoung Park,
A1 - Seungjin Lee,
A1 - Joo-Young Kim,
A1 - Jeong-Ho Woo,
A1 - Hoi-Jun Yoo,
PY - 2012
KW - Decision support systems
KW - Robustness
KW - Object recognition
KW - Multicore processing
KW - Network-on-a-chip
KW - Low power electronics
KW - SIFT
KW - object recognition
KW - attention
KW - attention-based object recognition
KW - network-on-chip
KW - multicore processor
KW - heterogeneous multicore
KW - object-recognition pipeline
KW - scale invariant feature transform
VL - 32
JA - IEEE Micro
ER -
 
Jinwook Oh, Korea Advanced Institute of Science and Technology
Gyeonghoon Kim, Korea Advanced Institute of Science and Technology
Injoon Hong, Korea Advanced Institute of Science and Technology
Junyoung Park, Korea Advanced Institute of Science and Technology
Seungjin Lee, Korea Advanced Institute of Science and Technology
Joo-Young Kim, Korea Advanced Institute of Science and Technology
Jeong-Ho Woo, Korea Advanced Institute of Science and Technology
Hoi-Jun Yoo, Korea Advanced Institute of Science and Technology
A new low-power object-recognition processor achieves real-time robust recognition, satisfying modern mobile vision systems' requirements. The authors introduce an attention-based object-recognition algorithm for energy efficiency, a heterogeneous multicore architecture for data- and thread-level parallelism, and a network on a chip for high on-chip bandwidth. The fabricated chip achieves 30 frames/second throughput and an average 320 mW power consumption on test 720p video sequences, yielding 640 GOPS/W and 10.5 nJ/pixel energy efficiency.
Index Terms:
Decision support systems,Robustness,Object recognition,Multicore processing,Network-on-a-chip,Low power electronics,SIFT,object recognition,attention,attention-based object recognition,network-on-chip,multicore processor,heterogeneous multicore,object-recognition pipeline,scale invariant feature transform
Citation:
Jinwook Oh, Gyeonghoon Kim, Injoon Hong, Junyoung Park, Seungjin Lee, Joo-Young Kim, Jeong-Ho Woo, Hoi-Jun Yoo, "Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems," IEEE Micro, vol. 32, no. 6, pp. 38-50, Nov.-Dec. 2012, doi:10.1109/MM.2012.90
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