Title: ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing Show Chinese title

Title: ViFusion：网络内张量融合用于可扩展视频特征索引

Abstract: Large-scale video feature indexing in datacenters is critically dependent on efficient data transfer. Although in-network computation has emerged as a compelling strategy for accelerating feature extraction and reducing overhead in distributed multimedia systems, harnessing advanced networking resources at both the switch and host levels remains a formidable challenge. These difficulties are compounded by heterogeneous hardware, diverse application requirements, and complex multipath topologies. Existing methods focus primarily on optimizing inference for large neural network models using specialized collective communication libraries, which often face performance degradation in network congestion scenarios. To overcome these limitations, we present ViFusion, a communication aware tensor fusion framework that streamlines distributed video indexing by merging numerous small feature tensors into consolidated and more manageable units. By integrating an in-network computation module and a dedicated tensor fusion mechanism within datacenter environments, ViFusion substantially improves the efficiency of video feature indexing workflows. The deployment results show that ViFusion improves the throughput of the video retrieval system by 8--22 times with the same level of latency as state-of-the-art systems. Show Chinese abstract

Abstract: 数据中心中的大规模视频特征索引严重依赖于高效的数据传输。尽管网络内计算已成为加速分布式多媒体系统中特征提取并减少开销的一种引人注目的策略，但充分利用交换机和主机级别的高级网络资源仍然是一项艰巨的挑战。这些困难因硬件异构性、多样的应用需求以及复杂的多路径拓扑结构而加剧。现有方法主要集中在使用专用的集体通信库优化大型神经网络模型的推理，但这些方法在网络拥塞场景下通常会遇到性能下降的问题。为克服这些限制，我们提出了ViFusion，这是一种通信感知的张量融合框架，通过将众多小型特征张量合并为整合且更易管理的单元，简化了分布式视频索引。通过在网络环境中集成网络内计算模块和专用的张量融合机制，ViFusion显著提高了视频特征索引工作流的效率。部署结果显示，ViFusion在与最先进的系统相同延迟的情况下，将视频检索系统的吞吐量提高了8到22倍。