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학술대회 OpenCL-Darknet: An OpenCL Implementation for Object Detection
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구용본, 유차영, 김성훈
International Workshop on Driving Computing Platform for Autonomous Vehicles (DrivComp) 2018, pp.631-634
17HS1800, 스마트카의 자율주행을 위한 실시간 센싱융합처리가 가능한 커넥티드 드라이빙 컴퓨팅 시스템 기술 개발, 김성훈
Object detection is a technology that deals with recognizing classes of objects and their location. It is used in many different areas, such as in face-detecting digital cameras, surveillance tools, or self-driving cars. These days, deep learning-based object detection approaches have achieved significantly better performance than the classic feature-based algorithms. Darknet [1] is a deep learning-based object detection framework, which is well known for its fast speed and simple structure. Unfortunately, like many other frameworks, Darknet only supports NVIDIA CUDA [2] for accelerating its calculations. For this reason, a user has only limited options for graphic card selection. OpenCL' (open computing language) [3] is an open standard for cross-platform, parallel programming of heterogeneous systems. It is available not only for CPUs, GPUs (graphics processing units), but also for DSPs (digital signal processors), FPGAs (field-programmable gate arrays) and other hardware accelerators. In this paper, we present the OpenCL-Darknet, which transforms the CUDA-based Darknet into an open standard OpenCL backend. Our goal was to implement a deep learning-based object detection framework that will be available for the general accelerator hardware and to achieve competitive performance compared to the original CUDA version. We evaluated the OpenCL-Darknet in AMD R7-integraged APU (accelerated processing unit) with OpenCL 2.0 and AMD Radeon RX560 with OpenCL 1.2 using a VOC 2007 dataset [4]. We also compared its performance with the original Darknet for NVIDIA GTX 1050 with CUDA 8.0 and cuDNN 6.0.
deep learning, object detection, OpenCL
KSP 제안 키워드
Accelerated Processing Unit, CUDA 8.0, Competitive performance, Cross-Platform, Detection Approaches, Detection Framework, Digital camera, Digital signal processor(DSP), Fast speed, Feature-based, Field Programmable Gate Arrays(FPGA)