MultiLens: A Multiobjective Adaptive DVFS Framework for Energy-Efficient DNN Inference

To tackle power management challenges in deep neural networks (DNNs), dynamic voltage and frequency scaling (DVFS) has gained attention for its ability to enhance energy efficiency without modifying DNN structures. However, current DVFS methods, which rely on historical data such as processor utilization and task load, suffer from issues like frequency ping-pong, response lag, and limited generalizability. These challenges are exacerbated by real-world scenarios that prioritize time, energy, or energy efficiency differently, making it even harder for existing methods to effectively configure DVFS under such multi-objective constraints or trade-offs. This paper presents MultiLens, a multi-objective adaptive DVFS framework. First, we propose a power-sensitive feature extraction method along with multi-objective constraint modeling to characterize DNN inference behavior. Second, critical power blocks are then identified through clustering based on inference behavior similarity, enabling adaptive DVFS instrumentation point settings. Moreover, to enhance the adaptability of multiple platforms and the flexibility of multiple scenarios, MultiLens integrates a complete deployment process. Experimental results demonstrate the effectiveness of the MultiLens in optimizing energy efficiency across different hardware platforms and deployment scenarios.