Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)

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FPGA-Based Deep Learning Accelerators for Healthcare Applications: Current Trends and Optimization Strategies with Intel oneAPI

Authors
S. Karthik1, *, K. Priyadarsini2, J. Jeba Sonia2
1Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
2Department of Data Science and Business Systems, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
*Corresponding author. Email: karthiks1@srmist.edu.in
Corresponding Author
S. Karthik
Available Online 30 June 2025.
DOI
10.2991/978-94-6463-754-0_60How to use a DOI?
Keywords
FPGA; Deep Learning; Healthcare; Intel oneAPI; Healthcare; Optimization Techniques
Abstract

The potential for improving healthcare applications has been demonstrated by the combination of deep learning with Field-Programmable Gate Array (FPGA) technology. This work presents an extensive analysis of FPGA-based deep learning accelerators, focusing on their implementation using Intel Arria 10 FPGAs. To enhance performance metrics, we investigate a number of optimization strategies, such as quantization and trimming. According to experimental data, our modified FPGA model reduces latency from 50 ms to 15 ms while achieving an accuracy of 93.2% for medical picture categorization. Furthermore, the Recurrent Neural Network (RNN) models outperform conventional CPU-based implementations in genomic data processing, demonstrating a 45-s processing time. The results show that FPGA designs with Intel One API support may significantly improve the efficacy and efficiency of deep learning applications used in the medical field.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
Series
Atlantis Highlights in Engineering
Publication Date
30 June 2025
ISBN
978-94-6463-754-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-754-0_60How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Karthik
AU  - K. Priyadarsini
AU  - J. Jeba Sonia
PY  - 2025
DA  - 2025/06/30
TI  - FPGA-Based Deep Learning Accelerators for Healthcare Applications: Current Trends and Optimization Strategies with Intel oneAPI
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 689
EP  - 701
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_60
DO  - 10.2991/978-94-6463-754-0_60
ID  - Karthik2025
ER  -