Digital Implementation of OS-ELM for Data Classification in Real-Time
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Field-programmable gate array (FPGA) has been used as a very effective hardware platform in different research area as it enhances the efficiency of the embedded module. The accessibility of minimized, fast circuitry for a artificial neural networks (ANNs) is the most important and utmost necessity for many critical applications. In this paper, a single layer feed-forward neural network (SLFN) named as online sequential extreme learning machine (OS-ELM) is conferred and realized in digital platform for real-world data classification. The digital employment of OS-ELM supports to form an efficient hardware unit for data classification in real-time as the classifier has high learning speed and promising accuracy. Finally, the digital architecture of OS-ELM is implemented on a Virtex-5 FPGA hardware platform to validate the feasibility, efficacy, and vitality of the proposed classifier in real-time.
KeywordsField-programmable gate array (FPGA) Artificial neural networks (ANNs) Single layer feed-forward neural network (SLFN) Online sequential extreme learning machine (OS-ELM) Digital architecture Hardware implementation
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