차량 검출용 CNN 분류기의 실시간 처리를 위한 하드웨어 설계
A Real-Time Hardware Design of CNN for Vehicle Detection
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초록
최근 딥 러닝을 중심으로 빠르게 발전하고 있는 기계학습 분류 알고리즘은 기존의 방법들보다 뛰어난 성능으로 인하여 주목받고 있다. 딥 러닝 중에서도 Convolutional Neural Network(CNN)는 영상처리에 뛰어나 첨단 운전자 보조 시스템(Advanced Driver Assistance System : ADAS)에서 많이 사용되고 있는 추세이다. 하지만 차량용 임베디드 환경에서 CNN을 소프트웨어로 동작시켰을 때는 각 Layer마다 연산이 반복되는 알고리즘의 특성으로 인해 수행시간이 길어져 실시간 처리가 어렵다. 본 논문에서는 임베디드 환경에서 CNN의 실시간 처리를 위하여 Convolution 연산 및 기타 연산들을 병렬로 처리하여 CNN의 속도를 향상시키는 하드웨어 구조를 제안한다. 제안하는 하드웨어의 성능을 검증하기 위하여 Xilinx ZC706 FPGA 보드를 이용하였다. 입력 영상은 36x36 크기이며, 동작주파수 100MHz에서 하드웨어 수행시간은 약 2.812ms로 실시간 처리가 가능함을 확인했다.
Abstract
Recently, machine learning algorithms, especially deep learning-based algorithms, have been receiving attention due to its high classification performance. Among the algorithms, Convolutional Neural Network(CNN) is known to be efficient for image processing tasks used for Advanced Driver Assistance Systems(ADAS). However, it is difficult to achieve real-time processing for CNN in vehicle embedded software environment due to the repeated operations contained in each layer of CNN. In this paper, we propose a hardware accelerator which enhances the execution time of CNN by parallelizing the repeated operations such as convolution. Xilinx ZC706 evaluation board is used to verify the performance of the proposed accelerator. For 36x36 input images, the hardware execution time of CNN is 2.812ms in 100MHz clock frequency and shows that our hardware can be executed in real-time.
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