Convolutional neural networks (CNNs) have had great success when applied to computer vision technology, and many application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) CNN accelerators have been proposed. These accelerators primarily focus on the acceleration of a single input, and they are not particularly optimized for video applications. In this article, we focus on the similarities between continuous inputs in video, and we propose a YOLOv3-tiny CNN FPGA accelerator using incremental operation. The accelerator can skip the convolution operation of similar data between continuous inputs. We also use the Winograd algorithm to optimize the conv

$3\times 3$

operator in the YOLOv3-tiny network to further improve the accelerator’s efficiency. Experimental results show that our accelerator achieved 74.2 frames/s on ImageNet ILSVRC2015. Compared to the original network without Winograd algorithm and incremental operation, our design provides a

$4.10\times $

speedup. When compared with other YOLO network FPGA accelerators applied to video applications, our design provided a

$3.13\times $

–

$18.34\times $

normalized digital signal processor (DSP) efficiency and

$1.10\times $

–

$14.2\times $

energy efficiency.

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