EVE：环境自适应神经网络模型在小功率能量收集系统中的应用
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
摘要：
物联网设备越来越多地使用神经网络模型来实现，以实现智能应用。由于能源的低维护成本和广泛可用性，从周围环境中收集能量的能量收集（EH）技术是用于为这些设备供电的电池的有前途的替代方案。然而，由能量采集器提供的功率较低，并且具有不稳定的固有缺点，因为其随周围环境而变化。本文提出了EVE，一个自动化的机器学习（autoML）的共同探索框架，以搜索所需的多个模型与共享的权重的能量收集物联网设备。这些共享模型显著减少了内存占用，并具有不同级别的模型稀疏性，延迟和准确性，以适应环境变化。一个有效的设备上的实现架构进一步发展，以有效地执行每个模型的设备上。提出了一种运行时模型提取算法，当特定模型模式被触发时，该算法能够以很小的开销提取单个模型，实验结果表明，在不进行剪枝和共享权值的情况下，EVE生成的神经网络模型的平均速度是基线模型的2.5倍。
英文摘要：
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources. However, the power provided by the energy harvester is low and has an intrinsic drawback of instability since it varies with the ambient environment. This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Those shared models incur significantly reduced memory footprint with different levels of model sparsity, latency, and accuracy to adapt to the environmental changes. An efficient on-device implementation architecture is further developed to efficiently execute each model on device. A run-time model extraction algorithm is proposed that retrieves individual model with negligible overhead when a specific model mode is triggered.Experimental results show that the neural networks models generated by EVE is on average 2.5X times faster than the baseline models without pruning and shared weights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
