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- W2885672017 abstract "Machine learning algorithms, such as Support Vector Machine (SVM) and Artificial Neural Networks, have been widely applied in many aspects of daily life. Low power/energy integrated circuit implementation of machine learning algorithms with high accuracy is however still a great challenge, which is critical for portable and wearable devices. In this paper, classifier ensemble is investigated for achieving low power classification. The classifier ensemble algorithm Adaboost is employed to combine multiple SVM classifiers with linear kernel to achieve high classification accuracy while reducing the hardware complexity. The proposed classifier ensemble is evaluated on the MNIST dataset by using a 45-nm CMOS technology. Compared to the traditional SVM classifier with second-order polynomial kernel, the proposed classifier ensemble achieves up to 45.7%, 20.3%, and 20.3% savings in total energy consumption, leakage power consumption, and area, respectively, while providing similar classification accuracy." @default.
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- W2885672017 date "2018-07-01" @default.
- W2885672017 modified "2023-09-25" @default.
- W2885672017 title "Achieving Low Power Classification with Classifier Ensemble" @default.
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- W2885672017 doi "https://doi.org/10.1109/isvlsi.2018.00014" @default.
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