Extreme learning machine (ELM) has shown to be an effective and low-power approach for real-time electrocardiography (ECG) anomaly detection. However, prior ELM inference chips are noise-prone and lacking in reconfigurability. In this article, we present an arbitrarily reconfigurable ELM inference engine fabricated in 40-nm CMOS technology for robust ECG anomaly detection. By combining Adaptive boosting (Adaboost) and Eigenspace denoising with ELM (AE-ELM), robust classification under noisy conditions is achieved and saves the number of required multiplications by 95.9%. For chip implementation, a reconfigurable VLSI architecture is designed to support arbitrary complexity of AE-ELM, accounting for dynamic change in application requirements. On the other hand, we propose to construct the input weight matrix of ELM as a Bernoulli random matrix, which further reduces the number of multiplications by 55.2%. For real-time detection, parallel computing is exploited to reduce the latency by up to 86.8%. Overall, the 0.21-mm ² AE-ELM inference engine shows its robustness against noisy signals and achieves $1.83\times$ AEE compared with the state-of-the-art ELM design.

中文翻译：

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用于鲁棒心电图异常检测的任意可重构极端学习机推理引擎

极限学习机（ELM）已被证明是一种有效的低功耗实时心电图（ECG）异常检测方法。但是，现有的ELM推理芯片容易产生噪声，并且缺乏可重构性。在本文中，我们介绍了一种采用40纳米CMOS技术制造的可任意配置的ELM推理引擎，用于强大的ECG异常检测。通过将自适应增强（Adaboost）和本征空间降噪与ELM（AE-ELM）结合使用，可以在嘈杂的条件下实现鲁棒的分类，并将所需的乘法次数节省95.9％。对于芯片实现，可重新配置的VLSI架构旨在支持AE-ELM的任意复杂度，并考虑了应用程序需求的动态变化。另一方面，我们建议将ELM的输入权重矩阵构造为Bernoulli随机矩阵，进而将乘法次数减少了55.2％。对于实时检测，利用并行计算可将延迟降低多达86.8％。总体而言，0.21毫米 ² AE-ELM推理引擎显示了其对噪声信号的鲁棒性并实现了 $ 1.83 \次$ AEE与最新的ELM设计进行了比较。