Hardware Implementation of a Deep Learning-based Autonomous System for Smart Homes using Field Programmable Gate Array Technology
Received: 12 July 2024 | Revised: 9 August 2024 | Accepted: 11 August 2024 | Online: 1 September 2024
Corresponding author: Ibraheem Kasim Ibraheem
Abstract
The current study uses Field-Programmable Gate Array (FPGA) hardware to advance smart home technology through a self-learning system. The proposed intelligent three-hidden layer system outperformed prior systems with 99.21% accuracy using real-world data from the MavPad dataset. The research shows that FPGA solutions can do difficult computations in seconds. The study also examines the difficulties of maximizing performance with limited resources when incorporating deep learning technologies into FPGAs. Despite these challenges, the research shows that FPGA-based solutions improve home technology. It promotes the integration of sophisticated learning algorithms into ordinary electronics to boost their intelligence.
Keywords:
FPGA, autonomous system, neural networks, deep learning, optimization techniques, scalability, hardware implementation, smart homeDownloads
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Copyright (c) 2024 Mohamed Tounsi, Ali Jafer Mahdi, Mahmood Anees Ahmed, Ahmad Taher Azar, Drai Ahmed Smait, Saim Ahmed, Ibraheem Kasim Ibraheem
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