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Cross-Platform Acceleration of Holographic Rendering in IoT Devices Using Open CL-Based Heterogeneous Computing

Mohammed E. SenoDepartment of Computer Sciences, College of Sciences, University of Al Maarif, Al Anbar, IraqSachin GuptaCentre for Research Impact and Outcome, Institute of Engineering and Technology, Chitkara University, Rajpura, IndiaShrabani MallickDepartment Of CSE, Dr. B. R. Ambekar Institute of Technology, Sri Vijaya Puram, IndiaAshit Kumar DuttaDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi ArabiaN. NeelimaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, IndiaMohit TiwariDepartment of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, IndiaPavitar Parkash SinghMittal School of Business, Lovely Professional University, Phagwara, IndiaNargiza KuzievaDepartment of Tax and Taxation, Tashkent State University of Economics, Tashkent, UzbekistanSheifali GuptaCentre for Research Impact and Outcome, Institute of Engineering and Technology, Chitkara University, Rajpura, India

IEEE Transactions on Consumer Electronicsjournal2025en

ABI

Annotatsiya

The integration of holographic technology into IoT-enabled consumer electronics offers transformative opportunities for immersive visualization and real-time interaction. However, the high computational demand of digital holographic reconstruction algorithms poses significant challenges for real-time deployment and cross-platform scalability, limiting their broader application in IoT ecosystems. To address these challenges, this paper presents an OpenCL-based heterogeneous acceleration framework that enhances the performance and portability of digital holographic reconstruction. By leveraging the parallel computing capabilities of CPU+GPU collaboration and data-parallel programming under the OpenCL architecture, the proposed method achieves significant improvements in computational efficiency. Experimental results across various hologram resolutions and GPU platforms demonstrate up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$54.2 \times $ </tex-math></inline-formula> overall speedup and a parallel acceleration ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$94.7 \times $ </tex-math></inline-formula> compared to CPU-only execution. Furthermore, the approach exhibits excellent scalability and cross-platform compatibility, making it well-suited for the real-time and embedded requirements of next-generation holographic IoT consumer devices.

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Advanced Optical Imaging Technologies

Identifikatorlar

DOI: 10.1109/tce.2025.3597749

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