Convolutional Neural Networks (CNN) and derivative architectures have been increasingly popular for image and signal processing applications such as detection and classification. Recently, a CNN architecture with a Wavelet Packet feature selector (Ultra-CNN) was introduced for Ultrasonic Non-Destructive Evaluation applications. This CNN based classifier manages to detect the presence of flaws with accuracy up to 92% using experimental data. In this study, an FPGA based Ultra-CNN design using high-level synthesis (HLS) is presented. Implementing the algorithm on a portable FPGA platform facilitates detection of ultrasonic flaws with high accuracy even when there is no access to high performance computation resources in the field. Unlike most other CNN designs used for pattern recognition in images, Ultra-CNN’s fully connected layers require more operations than its convolutional layers. In order to maximize the throughput, proposed design is optimized for both convolutional and fully connected layers. Therefore, we introduce a new design with two pipelined processors optimized for convolutional and fully connected layers, respectively. The results demonstrate highest utilization efficiency achieved compared to other CNN implementations and validate the low-cost, real-time operation of the design.

卷积神经网络 (CNN) 和衍生架构在检测和分类等图像和信号处理应用中越来越受欢迎。最近，为超声波无损评估应用引入了具有小波包特征选择器（Ultra-CNN）的 CNN 架构。这种基于 CNN 的分类器能够使用实验数据以高达 92% 的准确度检测缺陷的存在。在这项研究中，提出了一种使用高级合成 (HLS) 的基于 FPGA 的 Ultra-CNN 设计。即使在现场无法访问高性能计算资源的情况下，在便携式 FPGA 平台上实施该算法也有助于以高精度检测超声波缺陷。与大多数其他用于图像模式识别的 CNN 设计不同，Ultra-CNN 的全连接层比其卷积层需要更多的操作。为了最大化吞吐量，所提出的设计针对卷积层和全连接层进行了优化。因此，我们引入了一种新设计，其中有两个流水线处理器，分别针对卷积层和全连接层进行了优化。结果表明，与其他 CNN 实现相比，实现了最高的利用效率，并验证了该设计的低成本、实时操作。