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一种图像缩放的硬件加速电路

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"深度学习算法"已经广泛应用于图像识别领域,深度学习需要做大量的数据传输和卷积运算,对速度和数据带宽的要求极高.本文的项目背景是用现场可编程门阵列(FPGA)方案实现基于多任务卷积神经网络(MTCNN)的人脸识别算法,图像缩放是MTCNN算法中的一个步骤,图像通常缓存在双倍速率同步动态随机存储器(DDR)中,对图像数据的处理受DDR带宽的限制,如果采用普通的算法势必导致图像缩放耗时较长,影响整个深度学习的效率.本文以该项目中DDR的16字节位宽数据传输为例,阐述了采用"位置查表法加特殊桶形移位器"的硬件算法实现数据输入输出的并行流水操作,经过测试本算法的速度是常规算法的6倍左右.
A hardware acceleration circuit for image resizing
Deep learning algorithm has been widely used in the field of image recognition,deep learning needs to do a lot of data transmission and convolution operations,the speed and data bandwidth requirements are very high.The project background of this paper is to use FPGA scheme to realize face recognition based on MTCNN.Image scaling is a step in MTCNN algorithm.Images are usually cached in DDR,and the processing of image data is limited by the bandwidth of DDR.In this paper,the 16-byte wide data transmission of DDR in this project is taken as an example,and the hardware algorithm of"position lookup and bucket shifter"is used to realize the parallel pipeline-flow operation of data input and output After testing,the speed of this algorithm is about 6 times that of conventional algorithm.

image resizingdeep learningMTCNN hardware acceleration

庄国梁

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新大陆数字技术股份有限公司

图像缩放 深度学习 MTCNN硬件加速

2024

中国集成电路
中国半导体行业协会

中国集成电路

影响因子:0.144
ISSN:1681-5289
年,卷(期):2024.33(4)
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