Efficient logic architecture in training gradient boosting decision tree for high-performance and edge computing

This study proposes a logic architecture for the high-speed and power efficiently training of a gradient boosting decision tree model of binary classification. We implemented the proposed logic architecture on an FPGA and compared training time and power efficiency with three general GBDT software libraries using CPU and GPU. The training speed of the logic architecture on the FPGA was 26-259 times faster than the software libraries. The power efficiency of the logic architecture was 90-1,104 times higher than the software libraries. The results show that the logic architecture suits for high-performance and edge computing.

[1]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[2]  Christos-Savvas Bouganis,et al.  Accelerating Random Forest training process using FPGA , 2013, 2013 23rd International Conference on Field programmable Logic and Applications.

[3]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[4]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Qiang Wang,et al.  Benchmarking State-of-the-Art Deep Learning Software Tools , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[7]  Jing Wu,et al.  Visual Diagnosis of Tree Boosting Methods , 2018, IEEE Transactions on Visualization and Computer Graphics.

[8]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[9]  Eibe Frank,et al.  XGBoost: Scalable GPU Accelerated Learning , 2018, ArXiv.

[10]  Eibe Frank,et al.  Accelerating the XGBoost algorithm using GPU computing , 2017, PeerJ Comput. Sci..

[11]  Anna Veronika Dorogush,et al.  CatBoost: unbiased boosting with categorical features , 2017, NeurIPS.

[12]  Bingsheng He,et al.  Efficient Gradient Boosted Decision Tree Training on GPUs , 2018, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[13]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.