Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data
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With increasing capabilities of energy efficient systems, computational technology can be deployed, virtually everywhere. Machine learning has proven a valuable tool for extracting meaningful information from measured data and forms one of the basic building blocks of ubiquitous computing. In high-throughput applications, measurements are rapidly taken to monitor physical processes. This brings modern communication technologies to its limits. Therefore, only a subset of measurements, the interesting ones, should be further processed and possibly communicated to other devices. In this paper, we investigate architectural characteristics of embedded systems for filtering high-volume sensor data before further processing. In particular, we investigate implementations of decision trees and random forests for the classical von-Neumann computing architecture and custom circuits by the means of field programmable gate arrays.
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