Abstract
This work proposes a fully parallel hardware architecture of the Naive Bayes classifier to obtain high-speed processing and low energy consumption. The details of the proposed architecture are described throughout this work. Besides, a fixed-point implementation on a Stratix V Field Programmable Gate Array (FPGA) is presented and evaluated regarding the hardware area occupation, processing time (throughput), and dynamic power consumption. In addition, a comparative design analysis was carried out with state-of-the-art works, showing that the proposed implementation achieved a speedup of up to 104× and power savings of up to 107×-times while also reducing the hardware occupancy by up to 102×-times fewer logic cells.
Original language | English |
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Article number | 2565 |
Pages (from-to) | 2565 |
Number of pages | 15 |
Journal | Electronics |
Volume | 11 |
Issue number | 16 |
DOIs | |
Publication status | Published - 17 Aug 2022 |
Keywords
- FPGA
- Hardware
- Machine Learning
- Naive Bayes
- Parallel implementation