Error-Resilient Reconfigurable Boosting Extreme Learning Machine for ECG Telemonitoring Systems

Machine learning models have gained popularity of realizing Electrocardiography (ECG) monitoring systems. For constructing an inference device, providing both low latency and high accuracy is of a great concern. Extreme Learning Machine (ELM) is a single layer neural network that provides an effective solution for fast inference. In addition, the use of Adaptive Boosting (AdaBoost) algorithm can aggregate ELMs to enhance the overall learning ability. However, these computing units may encounter reliability issues that result from CMOS technology scaling and lead to a severe decline in performance. Hence, improving error resilience for a machine learning engine becomes a new design issue. This work presents a reliability-aware scheme for AdaBoost-based ELM. By exploiting the inherent redundancy in AdaBoost algorithm, it can strengthen the combination between different ELM classifiers. In an ECG-based atrial fibrillation detection case, the experimental results show that the proposed method can restore 71.4% of accuracy degradation caused by injected random bit-flip rate of 4 × 10
^-4
in computing units with small computational overhead. The classification engine is synthesized by TSMC 40nm CMOS technology, which can achieve extremely high classification rate.