Ketola, E.; Imtiaz, M. Systematic Review of Accelerating Time-Series Biosignal Machine Learning Processes Using GPU Architectures. Preprints2024, 2024050525. https://doi.org/10.20944/preprints202405.0525.v1
APA Style
Ketola, E., & Imtiaz, M. (2024). Systematic Review of Accelerating Time-Series Biosignal Machine Learning Processes Using GPU Architectures. Preprints. https://doi.org/10.20944/preprints202405.0525.v1
Chicago/Turabian Style
Ketola, E. and Masudul Imtiaz. 2024 "Systematic Review of Accelerating Time-Series Biosignal Machine Learning Processes Using GPU Architectures" Preprints. https://doi.org/10.20944/preprints202405.0525.v1
Abstract
Background: Time-series biosignal data, representative of a physiological process, is oftenapplied to time-sensitive machine learning applications that benefit from acceleration. Medicaland research applications that process biosignal data in real-time may utilize new hardwarearchitecture by switching from CPU to GPU devices to take advantage of the data processingspeedups.In order to utilize machine learning kernels that are typically employed by a CPUon a GPU, the machine learning kernel must be reimplemented using custom compilers thatcan take advantage of GPU architecture.Objectives:The primary objective is to evaluatethe speed of CPU-based machine learning algorithms commonly employed in biosignal process-ing and compare the speedup improvements obtained through GPU acceleration. Methods: Asystematic search was conducted across multiple databases to identify studies employing GPUacceleration in biosignal processing. Inclusion and exclusion criteria are defined for GPU accel-eration studies. In this literature review, 12 studies of GPU kernel development for traditionallyCPU-based kernels are analyzed. Results: It is found that a positive speedup occurs when usingGPU kernels over traditional CPU-based algorithms in all instances. The speedup of GPU overCPU performance ranges between 1.87 to 27018.27 times faster. Conclusions: This review willcontribute to the understanding of the role of GPU kernel development in biosignal processing,providing insights into performance improvements obtained by current GPU kernel development.The results indicate that GPU kernel development is a plausible direction to obtain real-timebiosignal-based systems.
Keywords
biosignals; data acceleration; GPU; kernel development; machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
