论文标题
AVAC：基于机器学习的自适应RRAM可变性 - 缘设备的控制器
AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices
论文作者
论文摘要
最近，边缘计算范式在行业和学术界都广受欢迎。现在，研究人员越来越多地针对提高性能并减少此类设备的能耗。最近的一些努力着重于使用新兴的RRAM技术来提高能源效率，这要归功于它们的泄漏属性和高集成密度。随着此类设备支持的应用程序的复杂性和动力学升级，静态RRAM控制器保持理想的性能变得困难。机器学习为此提供了有希望的解决方案，因此，这项工作着重于扩展此类控制器以允许动态参数更新。在这项工作中，我们建议使用自适应RRAM变异性控制器AVAC，该控制器会定期使用固定学习模型和梯度上升来定期更新等待缓冲区和批处理大小。 AVAC允许边缘设备适应不同的应用程序及其阶段，以提高计算性能并减少能耗。模拟表明，与静态控制器相比，使用现实生活中的医疗保健应用程序的痕迹在基于Raspberry-Pi的边缘部署上，与静态控制器相比，所提出的模型可提供高达29％的性能和能量下降19％。
Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.
