mlsanityData to 14 Oct 2026

Mitigating Compiler Fusion-Induced Power Bursts in Mobile NPU Inference as the Battery Depletes

arXiv 2607.16555 ↗First posted 17 Jul 2026cs.AR cs.AIcited by 0

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Abstract (arXiv)

Mobile devices increasingly rely on real-time NPU inference for camera and perception workloads. Under low-voltage conditions, however, a single inference can induce an instantaneous voltage droop in the power-delivery network, causing the power management integrated circuit to invoke dynamic voltage and frequency scaling (DVFS) and increase latency. We present a measurement study of this effect on a commercial smartphone. We show that aggressive operator fusion in a mobile NPU compiler can create monolithic superlayers whose concentrated execution produces large peak-current bursts. These bursts shift the DVFS-onset voltage upward and reduce the low-voltage operating margin. We further evaluate a practical black-box mitigation: a measurement-guided, pre-compilation graph rewrite that inserts barriers at selected peak-to-average power ratio hot spots to prevent harmful superlayer merging in the vendor NPU compiler. On Snapdragon 8 Gen 3 with MobileNetV4 at 768 x 768 resolution on ImageNet-1k, this method reduces peak current from 3.12 A to 1.94 A with 3.76% latency overhead, preserves stable latency deeper into the low-voltage regime, and shifts the inferred DVFS margin by approximately 173 mV.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 17 Jul 2026.

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14 of 28 references.

References · 14

  1. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  2. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 485
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  5. 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 290
  6. 2016Clipper: A Low-Latency Online Prediction Serving Systemcited by 191
  7. 2018NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applicationscited by 147
  8. 2016Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruningcited by 134
  9. 2024MobileNetV4 -- Universal Models for the Mobile Ecosystemcited by 99
  10. 2018Glow: Graph Lowering Compiler Techniques for Neural Networkscited by 75
  11. 2024Optimal Kernel Orchestration for Tensor Programs with Korchcited by 7
  12. 2025Dissecting the Impact of Mobile DVFS Governors on LLM Inference Performance and Energy Efficiencycited by 5
2 more
  1. 2025E4: Energy-Efficient DNN Inference for Edge Video Analytics Via Early-Exit and DVFScited by 2
  2. 2024AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devicescited by 1

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