PowerLens: An Adaptive DVFS Framework for Optimizing Energy Efficiency in Deep Neural Networks

To address the power management challenges in deep neural networks (DNNs), dynamic voltage and frequency scaling (DVFS) technology is garnering attention for its ability to enhance energy efficiency without modifying the structure of DNNs. However, current DVFS methods, which depend on historical information such as processor utilization and task computational load, face issues like frequency ping-pong, response lag, and poor generalizability. Therefore, this paper introduces PowerLens, an adaptive DVFS framework. Initially, we develop a power-sensitive feature extraction method for DNNs and identify critical power blocks through clustering based on power behavior similarity, thereby achieving adaptive DVFS instrumentation point settings. Then, the framework adaptively presets the target frequency for each power block through a decision model. Finally, through a refined training and deployment process, we ensure the framework's effective adaptability across different platforms. Experimental results confirm the effectiveness of the framework in energy efficiency optimization.