FPGA Implementation of an Ultrasonic Flaw Detection Algorithm Based on Convolutional Neural Networks

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.