CAS: Fusing DNN optimization & adaptive sensing for energy-efficient multi-modal inference

• Main Authors: WEERAKOON, Dulanga, SUBBARAJU, Vigneshwaran, LIM, Joo Hwee, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Deep Learning for Visual Perception; Embedded Systems for Robotic and Automation; Human-Robot Collaboration; RGB-D Perception; Vision and Sensor-Based Control; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9360; https://ink.library.smu.edu.sg/context/sis_research/article/10360/viewcontent/CAS_camready.pdf
• Institution: Singapore Management University

Intelligent virtual agents are used to accomplish complex multi-modal tasks such as human instruction comprehension in mixed-reality environments by increasingly adopting richer, energy-intensive sensors and processing pipelines. In such applications, the context for activating sensors and processing blocks required to accomplish a given task instance is usually manifested via multiple sensing modes. Based on this observation, we introduce a novel Commit-and-Switch ( CAS ) paradigm that simultaneously seeks to reduce both sensing and processing energy. In CAS , we first commit to a low-energy computational pipeline with a subset of available sensors. Then, the task context estimated by this pipeline is used to optionally switch to another energy-intensive DNN pipeline and activate additional sensors. We demonstrate how CAS's paradigm of interweaving DNN computation and sensor triggering can be instantiated principally by constructing multi-head DNN models and jointly optimizing the accuracy and sensing costs associated with different heads. We exemplify CAS via the development of the RealGIN-MH model for multi-modal target acquisition tasks, a core enabler of immersive human-agent interaction. RealGIN-MH achieves 12.9x reduction in energy overheads, while outperforming baseline dynamic model optimization approaches.