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학술대회 Implementation of Yolo-v2 Image Recognition and Other Testbenches for a CNN Accelerator
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저자
김찬, 김현미, 여준기, 조용철, 양정민, 정재훈, 신경선, 한진호, 최민석, 전인산, 권영수
발행일
201909
출처
International Conference on Consumer Electronics (ICCE) 2019 : Berlin, pp.242-247
DOI
https://dx.doi.org/10.1109/ICCE-Berlin47944.2019.8966213
협약과제
19HB1800, 인공지능프로세서 전문연구실, 권영수
초록
An implementation of Yolo-v2 image recognition and other testbenches for a deep learning accelerator is presented. This chip is the initial version of our on-going effort for a higher performance accelerator development. The accelerator is based on a systolic array and can handle convolution and max-pooling layer in a combined way or separately using 16 bit floating-point data. It also supports inner-product and LSTM layers. For demonstration and as one of the design verification testbenches, we implemented Yolo-v2 image recognition for 80 object classes. We converted the Yolo-v2 software to 16 bit floating point version and used it in the simulation and FPGA experiment during the chip development. Several other testbenches were designed and used to test various networks. In this paper, the accelerator's architecture and the Yolo-v2 implementation on our FPGA board including camera and LCD is explained, including the software implementation. Some analyses on the measured and estimated performance is shown.
키워드
Accelerator, CNN, Deep learning, Systolic array
KSP 제안 키워드
Design Verification, Estimated performance, FPGA Board, Floating-point data, Higher performance, Max-pooling, Systolic Array, deep learning(DL), image recognition, software implementation