Hardware-Efficient Residual Neural Network Execution in Line-Buffer Depth-First Processing

Deep convolutional neural networks (CNNs) provide State-of-the-Art (SotA) results in a variety of machine learning (ML) applications, such as image classification and object detection. The trends towards higher-resolution input images and large-scale model structures have brought drastic performance improvements for these tasks. Yet, these trends also pose increased stress on hardware resources. R...