通过协同调优批量大小与频率，实现低功耗设备上训练加速2.4倍。
Accelerated Training on Low-Power Edge Devices
- 联合调整GPU频率与训练批大小，兼顾功耗约束。
- 实测训练时间缩短2.4倍，接近最优性能。
- 适合边缘设备上需低功耗高效训练的场景。
在边缘设备上进行训练面临资源受限问题，尤其是功耗限制。现有先进方法通过降低GPU频率以满足功耗要求，导致训练时间显著增加。为加速训练，我们提出联合调节系统与应用参数（即GPU频率和训练任务的批大小），在满足设备功耗约束的前提下实现优化。引入一种新型跨层方法，结合批大小效率预测与设备性能分析，达成目标优化。真实硬件测试表明，该方法优于当前基于先进技术的基线方案，训练时间减少2.4倍，结果接近最优，同时大幅降低整体训练能耗，且不牺牲模型性能。
原文摘要 · Abstract (English)
Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level reduce the GPU frequency to enforce power constraints, leading to a significant increase in training time. To accelerate training, we propose to jointly adjust the system and application parameters (in our case, the GPU frequency and the batch size of the training task) while adhering to the power constraints on devices. We introduce a novel cross-layer methodology that combines predictions of batch size efficiency and device profiling to achieve the desired optimization. Our evaluation on real hardware shows that our method outperforms the current baselines that depend on state of the art techniques, reducing the training time by $2.4\times$ with results very close to optimal. Our measurements also indicate a substantial reduction in the overall energy used for the training process. These gains are achieved without reduction in the performance of the trained model.
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