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15 January 2024 Advancing machine learning tasks with field-programmable gate arrays: advantages, applications, challenges, and future perspectives
Da Ma
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Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 129830M (2024) https://doi.org/10.1117/12.3017278
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China
Abstract
This article comprehensively explores the applications, advantages, and challenges of using Field-Programmable Gate Arrays (FPGAs) to enhance machine learning tasks. It fills a gap in the existing literature by conducting a systematic review of the current FPGA utilization for the latest machine learning frameworks. The review provides valuable insights for researchers, highlighting the next steps regarding FPGA utilization in machine learning and its potential expansion to other areas. This article showcases case studies and research examples to demonstrate the effectiveness of FPGAs in different stages of machine learning, including data preprocessing, feature extraction, model training, and real-time neural network inference, and cutting-edge applications in the related field. Looking ahead, the future of FPGA research holds promise for further advancements in efficiency, performance, and the integration of FPGA technology with other hardware accelerators to meet the evolving demands of machine learning.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Da Ma "Advancing machine learning tasks with field-programmable gate arrays: advantages, applications, challenges, and future perspectives", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 129830M (15 January 2024); https://doi.org/10.1117/12.3017278
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KEYWORDS
Field programmable gate arrays
Machine learning
Neural networks
Deep learning
Energy efficiency
Computer programming
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