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Elastic Regression-Tree Learning in a Heterogeneous Computing Environment
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DOI：10.1109/JIOT.2019.2923884.png)
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摘要
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Embedded intelligence demands sensor functions with minimal operator interference. While computing resources in close proximity to a device are sometimes limited, more capable computing units suffer from latency in long-range data transmission. Here, we demonstrate a workflow on optimizing sensor performance for individual samples, in which we utilize an elastic regression-tree method to distribute computing tasks between an embedded chipset and a full-fledged workstation. A reconciliation of computing resources of different nature enables an efficient data acquisition in a completely automated manner, crucial to the deployment of edge intelligence in a broad context.
Keyword:
Constrained device
embedded computing
model uncertainty
regression tree learning
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8.9
论文数:
1.4W
被引数:
7.8W
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