中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234591
中文引用格式： 史興強(qiáng)，強(qiáng)小燕，鞏凱，等. 面向圖像語義分割的多類型卷積加速器設(shè)計(jì)[J]. 電子技術(shù)應(yīng)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