SMG: A System-Level Modality Gating Facility for Fast and Energy-Efficient Multimodal Computing

Achieving low-latency and high-efficiency multimodal computing (MMC) is crucial for deploying high-performance autonomous embedded systems (AES) that has limited energy budgets. However, existing methods have mainly focused on optimizing the computing phase and have overlooked the significant energy and latency overhead during the sensing phase. Therefore, we propose SMG, a system-level modality gating facility to optimize this. Our approach introduces a software-defined DSP gating technique that enables MMC tasks to bypass both the sensing and computing phases of unimportant modalities. We also propose a raw data-activated MMC mechanism that comprises a fast modality tester and adaptive modality executor, which adapts to the modality gating architecture and performs energy-efficient MMC. To evaluate SMG, we implement a prototype of SMG by integrating it into existing AES and analyze it with extensive multimodal video recognition workloads. Our experimental results show that SMG outperforms SOTA approaches by adaptively gating some DSP operations, resulting in substantial improvements in both energy consumption and task latency.