Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference
Deep neural network (DNN) inference on mobile devices often incurs high latency and energy consumption due to limited computing and memory resources. To enable energy-efficient DNN inference, most existing studies focus on dynamic voltage and frequency scaling (DVFS) for adjusting the computing frequency, while the impact of memory frequency on the inference performance has been greatly overlooked. In this paper, we consider the impact of memory frequency and computing frequency on DNN inference time, and jointly optimize these two frequencies together with communication resources for energy-efficient DNN inference. Based on a realistic inference time model, we formulate an optimization p...
| Authors | Yunchu Han, Zhaojun Nan, Sheng Zhou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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