The field of deep learning and data management is moving towards more efficient and scalable solutions. Researchers are exploring new techniques to reduce the computational overhead of deep neural networks, such as structure-aware automatic channel pruning and optimal kernel size determination. These approaches aim to improve model performance while minimizing computational costs. Additionally, there is a growing interest in developing scalable and distributed systems for data management, including vector databases and high-performance I/O libraries. These systems are designed to handle large volumes of data and provide efficient data analysis and retrieval. Noteworthy papers in this area include: Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks, which proposes a mathematically grounded framework for optimal kernel size determination, and HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search, which introduces a novel distributed vector database with a multi-granularity partition strategy.