MAMILS: A Memory-Aware Multiobjective Scheduler for Real-Time Embedded EEG Depression Diagnosis

作者：Fuze Tian, Lixin Zhang, Qi Pan, Jingyu Liu, Qinglin Zhao, Bin Hu · 发表于：IEEE Transactions on Parallel and Distributed Systems · 年份：2025 · DOI：10.1109/tpds.2025.3637175 · 被引用次数：4 · 研究领域：EEG and Brain-Computer Interfaces、Emotion and Mood Recognition、IoT and Edge/Fog Computing

Depression detection using Electroencephalogram (EEG) signals obtained from wearable medical-assisted diagnostic systems has become a well-established approach in the field of affective disorders. However, despite recent advancements, on-board Artificial Intelligence (AI) models still demand substantial computational resources, presenting significant challenges for deployment on resource-constrained wearable medical devices. Embedded Multi-core Processors (MPs) offer a promising solution for accelerating these models. However, the limited computational capabilities of embedded MPs, combined with the structural diversity of AI models, complicate resource allocation and increase associated costs. To address these challenges, we propose a Memory-Aware Multi-Objective Iterative Local Search (MAMILS) algorithm to optimize task scheduling, thereby improving the efficiency of AI model deployment on wearable EEG devices. Experimental results across seven AI models demonstrate that, the MAMILS approach yields substantial improvements in key performance indicators: Total Energy Consumption ($\bm {TEC}$) with an average reduction of 47.57%,$\bm {Makespan}$with an average reduction of 48.75%, and$\bm {Throughput}$with an average increase of 198.37%, all while maintaining satisfactory classification performance for both Machine Learning (ML) and Deep Learning (DL) models. Especially, on-board deployment of EEGNeX achieves an accuracy of 93.4%, sensitivity of 91.6%, and specificity of 95.8...