Machine Learning on FPGA for Real-time Applications and Beyond

Xuanmin Zheng

doi:10.47297/taposatWSP2633-456901.20240501

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Machine Learning on FPGA for Real-time Applications and Beyond

5卷第1期

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Machine Learning on FPGA for Real-time Applications and Beyond
Machine Learning on FPGA for Real-time Applications and Beyond
College of Engineering, Virginia Tech, Blacksburg, VA 24060, United state
2024年5卷第1期页码：5-10
纸质出版日期： 2024-06-30 ，
DOI：10.47297/taposatWSP2633-456901.20240501

Full txt

(2024). Machine Learning on FPGA for Real-time Applications and Beyond. 科技理论与实践（英文版）, 5(1), 5-10.

Zheng Xuanmin.Machine Learning on FPGA for Real-time Applications and Beyond[J].Theory and Practice of Science and Technology,2024,05(01):5-10.
(2024). Machine Learning on FPGA for Real-time Applications and Beyond. 科技理论与实践（英文版）, 5(1), 5-10. DOI： 10.47297/taposatWSP2633-456901.20240501.

Zheng Xuanmin.Machine Learning on FPGA for Real-time Applications and Beyond[J].Theory and Practice of Science and Technology,2024,05(01):5-10. DOI： 10.47297/taposatWSP2633-456901.20240501.

摘要

Abstract

Machine Learning (ML) has seen rapid advancements in recent years

with applications spanning various domains. In this article

we delve into the exciting realm of ML on Field-Programmable Gate Arrays (FPGA). We explore popular FPGA platforms tailored for ML

discuss the trade-offs compared to traditional GPU and CPU

highlight specific ML applications

and delve into FPGA design considerations. Additionally

we touch upon challenges and future directions in this ever-evolving field. By harnessing the power of FPGA

we unlock new possibilities for real-time

energy-efficient

and highly customizable ML solutions.

关键词

Keywords

Field-Programmable Gate ArrayFPGA platforms

references

Talib, M.A., Majzoub, S., Nasir, Q. et al. (2021) A Systematic Literature Review on Hardware Implementation of Artificial Intelligence Algorithms. The Journal of Supercomputing, 1897-1938.

Brahim B., David B. T., Wayne L. (2010) Comparing Performance and Energy Efficiency of FPGA and GPU for High Productivity Computing. In: 2010 International Conference on Field-Programmable Technology. Beijing. pp. 94-101.

Cong, J., Fang, Z., Lo, M., et, al. (2018). Understanding performance differences of FPGAs and GPUs. In: 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). Boulder. pp. 93-96.

Harada, K., Kanazawa, K., and Yasunaga, M. (2019). FPGA-Based Object Detection for Autonomous Driving System. In: 2019 International Conference on Field-Programmable Technology (ICFPT). Tianjin. pp. 465-468.

Azghadi, M.R., Lammie, C., Eshraghian, J.K. et al. (2020). Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications. IEEE Transactions on Biomedical Circuits and Systems, 14(6): 1138-1159.

Arora, A., Wei, Z., and John, L.K. (2020). Hamamu: Specializing FPGA for ML Applications by Adding Hard Matrix Multiplier Blocks. In: 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP). Manchester. pp. 53-60.

Li, S., Sun, K., Luo, Y., et, al. (2022). Novel CNN-Based AP2D-Net Accelerator: An Area and Power Efficient Solution for Real-Time Applications on Mobile FPGA. Electronics, 832.

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