Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset

Zhao, Yunyi; Xia, Yunjia; Loureiro, Rui; Zhao, Hubin; Dolinsky, Uwe; Yang, Shufan

Abstract

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applications. This work aims to create an adaptive acceleration framework for fNIRS motion artefact detection, which will be specifically designed for wearable devices. We evaluate the performance of the SVM classifier that has been implemented using SYCL on our fNIRS dataset across diverse devices and discuss the potential to accelerate more advanced motion artefact classifiers at the edge.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: IWOCL '23: International Workshop on OpenCL
• Start Date: Apr 18, 2023
• End Date: Apr 20, 2023
• Acceptance Date: Feb 24, 2023
• Online Publication Date: Apr 18, 2023
• Publication Date: 2023-04
• Deposit Date: Apr 11, 2023
• Publicly Available Date: Apr 11, 2023
• Publisher: Association for Computing Machinery (ACM)
• Book Title: IWOCL '23: Proceedings of the 2023 International Workshop on OpenCL
• DOI: https://doi.org/10.1145/3585341.3585380