Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning
Journal
IEEE Transactions on Circuits and Systems I: Regular Papers
Journal Volume
66
Journal Issue
12
Pages
4699-4712
Date Issued
2019
Author(s)
Abstract
Compressive sensing (CS) is applied to electrocardiography (ECG) telemonitoring system to address the energy constraint of signal acquisition in sensors. In addition, on-sensor-analysis transmitting only abnormal data further reduces the energy consumption. To combine both advantages, 'On-CS-sensor-analysis' can be achieved by compressed learning (CL), which analyzes signals directly in compressed domain. Extreme learning machine (ELM) provides an effective solution to achieve the goal of low-complexity CL. However, single ELM model has limited accuracy and is sensitive to the quality of data. Furthermore, hardware non-idealities in CS sensors result in learning performance degradation. In this work, we propose the ensemble of sub-eigenspace-ELM (SE-ELM), including two novel approaches: 1) We develop the eigenspace transformation for compressed noisy data, and further utilize a subspace-based dictionary to remove the interferences, and 2) Hardware-friendly design for ensemble of ELM provides high accuracy while maintaining low complexity. The simulation results on ECG-based atrial fibrillation show the SE-ELM can achieve the highest accuracy with 61.9% savings of the required multiplications compared with conventional methods. Finally, we implement this engine in TSMC 90 nm technology. The postlayout results show the proposed CL engine can provide competitive area- and energy-efficiency compared to existing machine learning engines. © 2004-2012 IEEE.
Subjects
Compressed learning; extreme learning machine; machine learning; noise tolerance; very large scale integration (VLSI)
SDGs
Other Subjects
Compressed sensing; Electrocardiography; Energy efficiency; Energy utilization; Engines; Knowledge acquisition; Learning systems; Metadata; VLSI circuits; Atrial fibrillation; Compressed learning; Compressive sensing; Conventional methods; Extreme learning machine; Learning performance; Noise tolerance; Telemonitoring systems; Machine learning
Type
journal article