ABSTRACT
Decision trees (DTs) offer a popular implementation choice for machine learning classifiers since they are highly interpretable and easy to use. Resource management decision overheads must be minimal in embedded systems to meet latency targets and deadline constraints. While the literature has preferred hardware architectures for DTs to meet latency targets, they are not suitable for ultra-low latency applications due to their data movement overheads despite the parallelism they offer. Therefore, we propose software optimization techniques for decision trees. The proposed DTs achieve lower than 50 ns latencies for depth 12, making them highly suitable for classification in embedded resource management.
- A. Krishnakumar et al. Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-core Systems. IEEE Trans. on CAD of Integr. Circuits and Syst., 39(11):4064--4077, 2020.Google ScholarCross Ref
- D. Lin, S. Talathi, and S. Annapureddy. Fixed Point Quantization of Deep Convolutional Networks. In Intl. Conf. on Machine Learning, pages 2849--2858, 2016.Google Scholar
- G. Mitra et al. Use of SIMD Vector Operations to Accelerate Application Code Performance on Low-powered ARM and Intel Platforms. In IEEE Intl. Symp. on Parallel & Distrib. Processing, Workshops and PhD Forum, pages 1107--1116, 2013.Google ScholarDigital Library
- D. Morawiec. sklearn-porter. Transpile Trained Scikit-learn Estimators to C, Java, JavaScript and others.Google Scholar
- F. Pedregosa et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825--2830, 2011.Google ScholarDigital Library
- A. L. Sartor et al. HiLITE: Hierarchical and Lightweight Imitation Learning For Power Management Of Embedded SoCs. IEEE CAL, 19(1):63--67, 2020.Google Scholar
- R. Struharik. Decision Tree Ensemble Hardware Accelerators for Embedded Applications. In 13th Intl. Synp. on Intell. Syst. and Inform., pages 101--106, 2015.Google ScholarCross Ref
- X. Tan et al. Performance Analysis of a Hardware Accelerator of Dependence Management for Task-based Dataflow Programming Models. In IEEE Intl. Symp. on Performance Analysis of Systems and Software, pages 225--234, 2016.Google Scholar
Recommendations
Using multi decision tree technique to improving decision tree classifier
The automatic classification systems, prediction and data mining are used in many applications marketing, finance, customer relationship management... using large databases. In this paper we describe a new data mining approach based on decision trees. ...
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
In this paper, we introduce two independent hybrid mining algorithms to improve the classification accuracy rates of decision tree (DT) and naive Bayes (NB) classifiers for the classification of multi-class problems. Both DT and NB classifiers are ...
Designing energy-efficient NoC for real-time embedded systems through slack optimization
DAC '13: Proceedings of the 50th Annual Design Automation ConferenceHard real-time embedded systems impose a strict latency requirement on interconnection subsystems. In the case of network-on-chip (NoC), this means each packet of a traffic stream has to be delivered within a time interval. In addition, with the ...
Comments