Abstract
Support vector machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of the SVM algorithm are very time-consuming and computationally intensive, which places a challenge on engineers to explore other hardware architectures than CPU, capable of performing real-time training and classifications while maintaining low power consumption in embedded systems. This paper proposes an overview of works based on the two most popular parallel processing devices: GPU and FPGA, with a focus on multiclass training process. Since different techniques have been evaluated using different experimentation platforms and methodologies, we only focus on the improvements realized in each study.
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Amezzane, I., Fakhri, Y., El Aroussi, M., Bakhouya, M. (2020). Hardware Acceleration of SVM Training for Real-Time Embedded Systems: Overview. In: Dos Santos, S., Maslouhi, M., Okoudjou, K. (eds) Recent Advances in Mathematics and Technology. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-35202-8_7
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DOI: https://doi.org/10.1007/978-3-030-35202-8_7
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