FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification
Susanta Kumar Rout1, Bhanja Kishor Swain2, Pradyut Kumar Biswal3

1Susanta Kumar Rout, Department of Electrical and Electronics Engineering, Siksha „O‟ Anusandhan Deemed to be University, Bhubaneswar, India.
2Bhanja Kishor Swain*, Department of Electrical Engineering, Siksha „O‟ Anusandhan Deemed to be University, Bhubaneswar, India.
3Pradyut Kumar Biswal, Department of Electronics and Telecommunication Engineering, International Institute of Information Technology, Bhubaneswar, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6551-6557 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1781109119/2019©BEIESP | DOI: 10.35940/ijeat.A1781.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.
Keywords: Artificial neural network (ANN), Field-programmable gate array (FPGA), Hardware architecture, Hardware implementation, Weighted online sequential extreme learning machine (WOS-ELM).