Edge AI: Systems Design and ML for IoT Data Analytics
Edge AI: Systems Design and ML for IoT Data Analytics
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DOI：
10.1145/3394486.3406479
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
R. Marculescu;Diana Marculescu;Ümit Y. Ogras
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文献类型：
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作者：
R. Marculescu;Diana Marculescu;Ümit Y. Ogras
随着大数据的爆炸式增长，人们常常忘记，现在的大部分数据都是在边缘生成的。具体来说，数据的主要来源是用户的终端设备，如手机、智能手表等，连接到互联网，也称为物联网（IoT）。这种“数据边缘”面临着与硬件约束、隐私感知学习和分布式学习（包括训练和推理）相关的几个新挑战。那么，我们可以使用哪些系统和机器学习算法来生成或利用边缘数据呢？网络科学可以帮助我们解决机器学习（ML）问题吗？物联网设备能否帮助患有某种形式残疾的人和许多其他人从健康监测中受益？在本教程中，我们将介绍与边缘计算相关的网络科学和ML技术，讨论ML系统（例如，模型压缩、量化、硬件/软件协同设计等）以及用于系统设计的ML（例如，运行时资源优化、用于边缘设备上的训练和推理的电源管理），并说明它们在解决具体物联网应用方面的影响。
With the explosion in Big Data, it is often forgotten that much of the data nowadays is generated at the edge. Specifically, a major source of data is users' endpoint devices like phones, smart watches, etc., that are connected to the internet, also known as the Internet-of-Things (IoT). This "edge of data" faces several new challenges related to hardware-constraints, privacy-aware learning, and distributed learning (both training as well as inference). So what systems and machine learning algorithms can we use to generate or exploit data at the edge? Can network science help us solve machine learning (ML) problems? Can IoT-devices help people who live with some form of disability and many others benefit from health monitoring? In this tutorial, we introduce the network science and ML techniques relevant to edge computing, discuss systems for ML (e.g., model compression, quantization, HW/SW co-design, etc.) and ML for systems design (e.g., run-time resource optimization, power management for training and inference on edge devices), and illustrate their impact in addressing concrete IoT applications.