Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular and have found their way into many portable electronics such as smartphones, tablets, and laptops. Furthermore, the use of biometrics enables secure access to private medical data, now collected in wearable devices such as smartwatches. In this work, we present an accurate low-power device authentication system that employs electrocardiogram (ECG) signals as the biometric modality. The proposed ECG processor consists of front-end signal processing of ECG signals and back-end neural networks (NNs) for accurate authentication. The NNs are trained using a cost function that minimizes intra-individual distance over time and maximizes inter-individual distance. Efficient low-power hardware was implemented by using fixed coefficients for ECG signal pre-processing and by using joint optimization of low-precision and structured sparsity for the NNs. We implemented two instances of ECG authentication hardware with 4X and 8X structurally-compressed NNs in 65nm LP CMOS, which consume low power of 62.37 microWatts and 75.41 microWatts for real-time ECG authentication with a low equal error rate of 1.36% and 1.21%, respectively, for a large 741-subject in-house ECG database. The hardware was evaluated at 10 kHz clock frequency and 1.2V voltage supply.

中文翻译：

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低功耗信号处理的ECG认证硬件设计以及低精度和结构化压缩的神经网络优化。

在现代身份验证系统中，越来越多地使用诸如面部特征，指纹和虹膜之类的生物识别技术。这些方法现在很流行，并且已经在许多便携式电子设备中找到了方法，例如智能手机，平板电脑和笔记本电脑。此外，使用生物识别技术可以安全地访问现在在可穿戴设备（如智能手表）中收集的私人医疗数据。在这项工作中，我们提出了一种精确的低功率设备认证系统，该系统采用心电图（ECG）信号作为生物特征识别方式。提出的ECG处理器包括ECG信号的前端信号处理和后端神经网络（NN），以进行准确的身份验证。使用成本函数对NN进行训练，该成本函数会随着时间的推移最小化个体内部距离并最大化个体间距离。通过使用固定系数进行ECG信号预处理，以及对NN进行低精度和结构化稀疏度的联合优化，可以实现高效的低功耗硬件。我们在65nm LP CMOS中实现了带有4X和8X结构压缩神经网络的ECG身份验证硬件的两个实例，它们消耗62.37微瓦和75.41微瓦的低功耗进行实时ECG身份验证，其均等错误率分别为1.36％和1.21％，分别针对大型741个受试者的内部ECG数据库。在10 kHz时钟频率和1.2V电源电压下评估了硬件。对于大型的741个内部ECG数据库，实时ECG身份验证的功耗分别为62.37微瓦和75.41微瓦，均等错误率分别为1.36％和1.21％。在10 kHz时钟频率和1.2V电源电压下评估了硬件。对于大型的741个内部ECG数据库，实时ECG身份验证的功耗分别为62.37微瓦和75.41微瓦，均等错误率分别为1.36％和1.21％。在10 kHz时钟频率和1.2V电源电压下评估了硬件。