FPGA Accelerator for Gradient Boosting Decision Trees

Alcolea, Adrián; Resano, Javier

doi:10.3390/electronics10030314

Cited by 34 publications

(17 citation statements)

References 14 publications

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“…It is also claimed that GBA is an efficient method on computing the predicted values such as total amount paid for each claim and payment schedule. Alcolea and Resano [22] also implemented the GBA in an accelerator design to optimize the execution of the decision trees in reducing the energy consumption. It was implemented in a Field Programmable Gate Array (FPGA) for embedded systems, and tested it with a relevant case-study: pixel classification of hyperspectral images.…”

Section: Related Workmentioning

confidence: 99%

Detection of Earthquake Damages from Satellite Images using Gradient Boosting Algorithm with Decision Trees as Base Estimator

Acula¹

2022

actamanil

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The Philippines experiences an average of twenty (20) earthquakes every day and 100 to 150 felts every year. With the aim to help the government in mitigation of the potential impact of the earthquake in the country, this research explored the detection of earthquake damages from the satellite images. Since Gradient Boosting Algorithm is considered one of the efficient and powerful predictive models, this method was employed in this research to classify if the satellite images before and after the earthquake brought damages in the infrastructure of affected areas. The satellite images used in the research were downloaded from Landsat 8 via Google Earth pro, which focused on a Magnitude 7.6 earthquake in Eastern Samar on August 31, 2012. The research used image augmentation such as rotation, shearing and flipping before the image extraction. The extracted features were used as classifiers of the models and were separated into 80:20, 70:30 and 60:20 ratios for training and testing sets respectively. The detection of damages was evaluated five (5) times using different n-estimators or numbers of trees {10, 20, 30, 40, 50}. The experiment concluded that Gradient Boosting Algorithm is an efficient model for classification and detection of earthquake damages using satellite image data, with an optimal detection accuracy of 85.71% and 100% without and with image augmentation.

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Section: Related Workmentioning

confidence: 99%

Detection of Earthquake Damages from Satellite Images using Gradient Boosting Algorithm with Decision Trees as Base Estimator

Acula¹

2022

actamanil

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“…Many recent works, including [19] [20], have implemented DNNs to classify diabetic patients by transforming the problem into a classification problem, the deep learning model's success area. In this case study, we use the Pima Indians diabetes dataset.…”

Section: Case Studymentioning

confidence: 99%

Generic Design Flow of Pipelined Hardware Implementation of Deep Neural Networks

Tourad¹,

Eleuldj²

2022

INDJCSE

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DNNs (Deep Neural Networks) have solved various deep learning tasks, including classification problems, natural language processing, and speech recognition. However, this success comes with increased computational and memory requirements. Furthermore, recent deep learning research indicates that hardware implementations such as FPGAs (Field Programmable Gate Arrays) are preferable for implementing DNNs, and they fulfill the requirements due to the integrated circuits with programmable logic gates and connections. This technique offers hardware implementation flexibility, which makes it appealing for a wide range of applications, and that flexibility differs from the standard circuit. As a result, FPGAs are becoming increasingly popular as a hardware solution for accelerating systems and processes. This article presents a generic version of the design flow for automatically implementing DNN models on hardware by generating pipelined HDL codes, which can overcome the implementation problem. The article compares the design flow to other recent similar tools. The design flow is validated using a DNN to detect the diabetic patient from the Pima Indians dataset classification. This paper shows a high performance by reducing the latency by 4x the non-pipelined VHDL code and 1000x the software implementation using the TensorFlow framework without affecting the model's accuracy. Also, this design flow can serve in the early prediction of diabetes in the future-finally, a presentation of the conclusion and future works.

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“…It has been demonstrated in earlier studies that it is possible to deploy XGBoost models to embedded processors 18 . This ability ensures the usage of this method for practical scenarios as well.…”

Section: Ageing Setup Data Acquisition and Processingmentioning

confidence: 99%

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Celen¹,

Ozcelik²,

Turgut³

et al. 2022

Open Res Europe

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost’s error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

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FPGA Accelerator for Gradient Boosting Decision Trees

Cited by 34 publications

References 14 publications

Detection of Earthquake Damages from Satellite Images using Gradient Boosting Algorithm with Decision Trees as Base Estimator

Detection of Earthquake Damages from Satellite Images using Gradient Boosting Algorithm with Decision Trees as Base Estimator

Generic Design Flow of Pipelined Hardware Implementation of Deep Neural Networks

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

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