Design and FPGA Implementation of Lite Convolutional Neural Network Based Hardware Accelerator for Ocular Biometrics Recognition Technology

Wei-Che Sun, Chih-Peng Fan, Chung-Bin Wu
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Abstract

In this study, the effective low-complexity Convolutional Neural Network (CNN) inference network is implemented by the FPGA-based hardware accelerator for the biometric authentications. After the labeling processes, the eye images with partial iris and sclera zones are used to train and test the LeNet-based Lite-CNN model. Then the lightweight CNN classifier is rapidly prototyped via FPGA for hardware acceleration. Through testing, the proposed Lite-CNN model achieves up to 98% recognition accuracy with the eye images. Compared with the software-based implementation, the proposed Lite-CNN hardware accelerator provides similar detection accuracy, and the inference time of 0.0246 seconds is accelerated about 377 times on the Xilinx ZCU102 FPGA platform. Besides, compared with the previous FPGA implementation by the high level synthesis design, the proposed hardware acceleration design performs the computing speed more than about 92 times.
基于Lite卷积神经网络的眼部生物特征识别硬件加速器的设计与FPGA实现
在本研究中,采用基于fpga的硬件加速器实现了有效的低复杂度卷积神经网络(CNN)推理网络,用于生物识别认证。经过标记处理后,使用部分虹膜和巩膜区域的眼睛图像来训练和测试基于lenet的Lite-CNN模型。然后通过FPGA快速原型化轻量级CNN分类器,实现硬件加速。通过测试,本文提出的Lite-CNN模型对人眼图像的识别准确率高达98%。与基于软件的实现相比,本文提出的Lite-CNN硬件加速器提供了相似的检测精度,并且在Xilinx ZCU102 FPGA平台上将0.0246秒的推理时间加快了约377倍。此外,与以往采用高级综合设计的FPGA实现相比,所提出的硬件加速设计将计算速度提高了约92倍。
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