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New method optimizes DNN inference for energy efficiency on mobile devices

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for optimizing energy efficiency in deep neural network (DNN) inference on mobile devices. The approach jointly optimizes both computing and memory frequencies, along with communication resources, to minimize energy consumption while adhering to deadline constraints. For local inference, a near-optimal closed-form solution was derived using convex optimization, achieving performance within 2.5% of the optimal solution and reducing energy consumption by up to 10.4% compared to existing methods. AI

IMPACT This research could lead to more energy-efficient AI applications on mobile devices, extending battery life and enabling more complex on-device processing.

RANK_REASON The cluster contains an academic paper detailing a new method for optimizing DNN inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method optimizes DNN inference for energy efficiency on mobile devices

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The cluster contains an academic paper detailing a new method for optimizing DNN inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yunchu Han, Zhaojun Nan, Sheng Zhou, Zhisheng Niu ·

    Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference

    arXiv:2608.13863v1 Announce Type: new Abstract: 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 v…

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