반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
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OpenCV 내장 CPU 및 GPU 함수를 이용한 DNN 추론 시간 복잡도 분석

Performance Analysis of DNN inference using OpenCV Built in CPU and GPU Functions

  • 박천수 (성균관대학교 컴퓨터교육과)
  • 투고 : 2022.02.24
  • 심사 : 2022.03.25
  • 발행 : 2022.03.31
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초록

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

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참고문헌

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