High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor

Antony, A and Devi, A and Varghese, K (2021) High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor. In: 6th International Conference for Convergence in Technology, I2CT 2021,, 2-4Apr 2021, Pune.

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Abstract

Hoeffding tree algorithm is a popular online decision tree algorithm capable of learning from huge data streams. The algorithm involves complex time consuming computations in the leaves of the tree for each data instance. These computations involve a lot of parallelisms which can be exploited and implemented in a field programmable gate array to achieve speedup. This paper presents a hardware accelerator for Hoeffding tree algorithm with adaptive naive bayes predictor in the leaves. The proposed system is capable of accelerating data streams with both nominal and numeric attributes using minimum hardware resources for huge datasets. It is implemented on a Xilinx VC707 board based on Virtex-7 XC7VX485T field programmable gate array. The implemented system is about 9x faster than StreamDm(C++), a well known reference software implementation for the standard forest cover type dataset. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	2021 6th International Conference for Convergence in Technology, I2CT 2021
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	C++ (programming language); Classifiers; Computer hardware description languages; Computer software; Data streams; Decision trees; Logic gates, Forest cover; Hardware accelerators; Hardware resources; High throughput; Naive bayes; Online decisions; Reference software; Tree algorithms, Field programmable gate arrays (FPGA)
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	17 Aug 2021 09:09
Last Modified:	17 Aug 2021 09:09
URI:	http://eprints.iisc.ac.in/id/eprint/69181

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