Abstract
Nowadays, in a broad range of application areas, the daily data production has reached unprecedented levels. This data origins from multiple sources, such as sensors, social media posts, digital pictures and videos and so on. The technical and scientific issues related to the data booming have been designated as the “Big Data” challenges. To deal with big data analysis, innovative algorithms and data mining tools are needed in order to extract information and discover knowledge from the continuous and increasing data growing. In most of data mining methods the data volume and variety directly impact on computational load. In this paper we illustrate a hardware architecture of the decision tree predictor, a widely adopted machine learning algorithm. In particular we show how it is possible to automatically generate a hardware implementation of the predictor module that provides a better throughput that available software solutions.
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Amato, F., Barbareschi, M., Casola, V., Mazzeo, A., Romano, S. (2013). Towards Automatic Generation of Hardware Classifiers. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_14
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DOI: https://doi.org/10.1007/978-3-319-03889-6_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03888-9
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