MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding Their Hardware-Software Implications

作者：Cheng Xu, Xiaofeng Hou, Jiacheng Liu, Chao Li, Tianhao Huang, Xiaozhi Zhu, M. Yuezhen Niu, Lingyu Sun, Peng Tang, Tongqiao Xu, Kwang‐Ting Cheng, Minyi Guo · 年份：2023 · DOI：10.1109/iiswc59245.2023.00014 · 被引用次数：9 · 研究领域：Advanced Neural Network Applications、Ferroelectric and Negative Capacitance Devices、Multimodal Machine Learning Applications

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of distributed computing applications from traditional multimedia systems to emerging autonomous edge systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. Existing benchmarks either target uni-modal DNNs or only focus on the algorithm characteristics of multi-modal DNNs. There lacks representative benchmark suites that provide comprehensive system and architecture level analysis of multi-modal networks.To advance the understanding of these multi-modal DNN workloads and facilitate related research, we present MM-Bench, an open-source, end-to-end benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. We then use MMBench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We demonstrate their unique characteristics of clear multi-stage execution, frequent synchronization and high heterogeneity, which distinguish th...