Sparse CNN

Sparse convolutional neural networks (CNNs) aim to improve the efficiency and speed of CNNs by leveraging the inherent sparsity in data and model weights, reducing computational costs and memory requirements. Current research focuses on developing novel sparse CNN architectures, such as those employing adaptive receptive fields and uniform block pruning, to enhance accuracy and performance, particularly in applications like 3D semantic segmentation and event-based vision. These advancements are significant because they enable the deployment of deep learning models on resource-constrained devices and accelerate inference times in various domains, including eye-tracking, autonomous driving, and high-energy physics.