中图分类号：TP391 文献标志码：A DOI: 10.16157/j.issn.0258-7998.234591
中文引用格式： 史兴强，强小燕，巩凯，等. 面向图像语义分割的多类型卷积加速器设计[J]. 电子技术应用，2023，49(12)：26-30.
英文引用格式： Shi Xingqiang，Qiang Xiaoyan，Gong Kai，et al. Design of multi type convolution accelerator for image semantic segmentation[J]. Application of Electronic Technique，2023，49(12)：26-30.

Design of multi type convolution accelerator for image semantic segmentation

Abstract： In order to improve accuracy, image semantic segmentation networks often use complex convolutional layers as the basic feature extraction units. The different types of convolutions present in these convolutional layers increase the difficulty of parallel acceleration computation for the network. A parallel computing accelerator based on FPGA for multi type convolutions is proposed to meet the accelerated computing requirements of different types of convolutions in semantic segmentation networks. Firstly, the calculation principle of convolution is analyzed. Then, based on the basic operation principles of different convolution types, a processing unit for multi multiplication parallel computing is constructed. The convolution is accelerated through multi processing unit parallelism, data reuse, and PIPELINE method. The experimental results show that for specific size feature maps, using the proposed convolutional accelerator design method can achieve a maximum speed increase of 113 times.

Key words : image semantic segmentation；multi type convolutions；FPGA；computational acceleration