Hierarchical and Distributed Machine Learning Inference Beyond the Edge
Hierarchical and Distributed Machine Learning Inference Beyond the Edge
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DOI：
10.1109/icnsc.2019.8743164
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Anthony Thomas;Yunhui Guo;Yeseong Kim;Baris Aksanli;Arun Kumar;Tajana Simunic
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作者：
Anthony Thomas;Yunhui Guo;Yeseong Kim;Baris Aksanli;Arun Kumar;Tajana Simunic
具有异构传感器的网络应用程序是一个不断增长的数据源。此类应用程序使用机器学习 (ML) 进行实时预测。目前，所有传感器的特征都收集在基于云的集中层中，以形成用于机器学习预测的整个特征向量。这种方式通信成本高，浪费能源，而且经常成为网络瓶颈。在这项工作中，我们研究了一种替代方法，通过将机器学习推理计算从云端“推”到物联网设备的层次结构上来缓解此类问题。我们的方法提出了一项新的技术挑战，即“重写”机器学习推理计算，以将其分解到设备网络上，而不会显着降低预测准确性。我们为一些保持准确性的流行模型引入了新颖的精确因式分解算法。我们还创建了其他模型的新颖近似变体，以提供高精度。对常见物联网设备的测量表明，与将所有数据发送到云端相比，能源使用和延迟可分别减少高达 63% 和 67%，而不会降低准确性。
Networked applications with heterogeneous sensors are a growing source of data. Such applications use machine learning (ML) to make real-time predictions. Currently, features from all sensors are collected in a centralized cloud-based tier to form the whole feature vector for ML prediction. This approach has high communication cost, which wastes energy and often bottlenecks the network. In this work, we study an alternative approach that mitigates such issues by “pushing” ML inference computations out of the cloud and onto a hierarchy of IoT devices. Our approach presents a new technical challenge of “rewriting” an ML inference computation to factor it over a network of devices without significantly reducing prediction accuracy. We introduce novel exact factoring algorithms for some popular models that preserve accuracy. We also create novel approximate variants of other models that offer high accuracy. Measurements on a common IoT device show that energy use and latency can be reduced by up to 63% and 67% respectively without reducing accuracy relative to sending all data to the cloud.