Development of a Verilog-Based System for Pattern Detection Using Convolutional Neural Networks

Pattern detection is a foundational element in computer vision, crucial for advancements in autonomous driving, surveillance, and numerous other fields. Convolutional Neural Networks (CNNs) have significantly enhanced pattern detection by improving both accuracy and efficiency. This paper aims to develop a Verilog-based system for pattern detection using CNNs, focusing on detecting and counting occurrences of a specified test pattern within input images. The CNN architecture employs Convolutional Layers for feature extraction and Max Pooling Layers for spatial downsampling, both essential in image processing. Through iterative weight adjustments during training, the CNN learns to recognize patterns similar to the test pattern. After training, the system evaluates unseen images, predicting the presence and count of test pattern instances. To optimize performance, the system incorporates approximate adders to reduce area and gate count, thereby enhancing efficiency. These optimizations enable the system to achieve high performance while maintaining computational efficiency, providing detection and counting of specified patterns in images. This capability offers valuable insights and practical solutions for various real-world applications.