Real-Time Object Detection Towards High Power Efficiency

PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)(2018)

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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.
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Key words
real-time object detection,mobile applications,optimizing techniques,mobile GPU platforms,object detection algorithms,computer vision tasks,image object detection,CNN methods,power consumption,low-power image recognition challenge,energy efficiency,power efficiency,convolutional neural network,automatic driving,LPIRC,FPGA,hardware platforms
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