FPGA-Driven Machine Learning Models: Trends, Challenges, and Implementations

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : Sattenapalli Kalyani, Vydeki D

10.14445/23488549/IJECE-V12I10P116

How to Cite?

Sattenapalli Kalyani, Vydeki D, "FPGA-Driven Machine Learning Models: Trends, Challenges, and Implementations," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 10, pp. 196-211, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I10P116

Abstract:

FPGAs represent a robust platform for accelerating ML algorithms because they enable parallel computation and short latency while minimizing power usage. Every aspect follows computerization, and most items achieve smart functionality at present. The IoT technology of the present allows network connection through the use of IoT platforms for objects. IoT defines an innovative information system of linked devices that perform automated exchanges between equipment independent of human input. IoT systems require flexible platforms. The connection capability of IoT devices to external environments depends on Field Programmable Gate Array (FPGA) technology, which provides easy user access using low-power systems with minimal delays and exceptional precision. The scalability feature of FPGAs allows SoC implementation since designers can place various hardware clocks onto one single chip. The FPGA functions as a particular type of programmable mainframe since it receives indicators through its input pins before transforming them into outputs at its output pins. This evaluation explores recent FPGA implementation methods of ML algorithms with a specific focus on Support Vector Machines (SVMs) and their classification precision. The research evaluates various hardware system designs while evaluating their performance tradeoffs and identifies noteworthy research areas for improvement. The final part addresses directions for enhancing FPGA-based ML implementations.

Keywords:

SVM, Embedded Systems, FPGA, Hardware Architecture, System on Chip.

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