.png){kind=icon}
返回
TinyML Meets IoT: A Comprehensive Survey
.png){kind=icon}
PRE
AI
DOI：10.1016/j.iot.2021.100461.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rapid growth in miniaturization of low-power embedded devices and advancement in the optimization of machine learning (ML) algorithms have opened up a new prospect of the Internet of Things (IoT), tiny machine learning (TinyML), which calls for implementing the ML algorithm within the IoT device. TinyML framework in IoT is aimed to provide low latency, effective bandwidth utilization, strengthen data safety, enhance privacy, and reduce cost. Its ability to empower the IoT device to reliably function without consistent access to the cloud services while delivering accurate ML services makes it a promising option for IoT applications seeking costeffective solutions. Especially in settings where inadequate connectivity is common, TinyML aims to provide on-premise analytics which will add substantial benefit to IoT services. In this article, we introduce the definition of TinyML and provide background information on diverse related technologies stating their strengths and weaknesses. We then show how TinyML-as-aservice is implemented through efficient hardware-software co-design. This article also introduces the role of 5G in TinyML-IoT scenario. Furthermore, it touches on the recent progress in TinyML research in both academia and industry along with future challenges and opportunities. We believe that this review will serve as an information cornerstone for the IoT research community and pave the way for further research in this direction.
Keyword:
Internet of Things (IoT)
tiny machine learning (TinyML)
hardware-software co-design
期刊
IF:
7.6
论文数:
2.0K
被引数:
6.9K
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容