Paper Info
Title
Real-Time Object Detection Towards High Power Efficiency
Abstract
In recent years, Convolutional Neural Network (CNN) has been widely applied in computer vision tasks and has achieved significant improvement in image object detection. The CNN methods consume more computation as well as storage, so GPU is introduced for real-time object detection. However, due to the high power consumption of GPU, it is difficult to adopt CPU in mobile applications like automatic driving. The previous work proposes some optimizing techniques to lower the power consumption of object detection on mobile GPI) or FPGA. In the first Low-Power Image Recognition Challenge (LPIRC), our system achieved the best result with mAP/Energy on mobile GPU platforms. We further research the acceleration of detection algorithms and implement two more systems for real-time detection on FPGA with higher energy efficiency. In this paper, we will introduce the object detection algorithms and summarize the optimizing techniques in three of our previous energy efficient detection systems on different hardware platforms for object detection.
Year
Venue
Field
2018
PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Electrical efficiency,Object detection,Convolution,Efficient energy use,Computer science,Convolutional neural network,Field-programmable gate array,Real-time computing,Acceleration,Computation
DocType
ISSN
Citations 
Conference
1530-1591
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Jincheng Yu131519.49
Kaiyuan Guo233219.19
Yiming Hu363944.91
Xuefei Ning4256.37
Jiantao Qiu530716.48
Huizi Mao6127941.30
Song Yao743821.18
Tianqi Tang834219.66
Boxun Li957131.13
Yu Wang102279211.60
Huazhong Yang112239214.90