[2603.21908] SparseDVFS: Sparse-Aware DVFS for Energy-Efficient Edge Inference

Computer Science > Machine Learning arXiv:2603.21908 (cs) [Submitted on 23 Mar 2026] Title:SparseDVFS: Sparse-Aware DVFS for Energy-Efficient Edge Inference Authors:Ziyang Zhang, Zheshun Wu, Jie Liu, Luca Mottola View a PDF of the paper titled SparseDVFS: Sparse-Aware DVFS for Energy-Efficient Edge Inference, by Ziyang Zhang and 2 other authors View PDF HTML (experimental) Abstract:Deploying deep neural networks (DNNs) on power-sensitive edge devices presents a formidable challenge. While Dynamic Voltage and Frequency Scaling (DVFS) is widely employed for energy optimization, traditional model-level scaling is often too coarse to capture intra-inference variations, whereas fine-grained operator-level scaling suffers from prohibitive performance degradation due to significant hardware switching latency. This paper presents SparseDVFS, a fine-grained, sparse-aware DVFS framework designed for energy-efficient edge inference. Our key insight is that operator sparsity is a primary metric for hardware frequency modulation. By distinguishing between compute-bound dense operators and memory-bound sparse operators, the system can apply specialized frequency triplets to maximize energy efficiency. To overcome switching overheads and component interference, SparseDVFS incorporates three key innovations: (1) an offline modeler that established a deterministic mapping between operator sparsity and optimal frequency triplets (CPU/GPU/EMC) via white-box timeline analysis; (2) a runtime g...