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@article{171741,
        author = {Santhoshini P and Sadha B and Nikshitha V and Murugavel Chetty},
        title = {Performance Review of Deep Learning on FPGAs and GPUs: Transforming Next-Generation Industrial Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {953-959},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171741},
        abstract = {Industries are increasingly focusing on sophisticated components for the development of multi-purpose machinery and devices, driven by the habitual integration of engineering and technology. Significant advancements in the fields of electronics, computer science, and automation, particularly in clustering, deep learning, neural networks, and machine learning techniques, are being leveraged. These advancements are being implemented in modern components such as Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). This review paper comprehensively examines the development and application of deep learning algorithms in FPGAs and GPUs. We explore the latest trends, compare the performance and efficiency of various approaches, and discuss the implications of these technologies for future industrial applications. The findings highlight the potential for improved computational efficiency and the acceleration of complex tasks, which could significantly impact the design and functionality of next-generation machinery and devices},
        keywords = {VLSI, Deep Learning, FPGA, Neural Networks.},
        month = {January},
        }