Instituto de Telecomunicações (IT), 1049-001 Lisbon, Portugal.
Instituto Superior Técnico (IST), Universidade de Lisboa (UL), 1050-049 Lisbon, Portugal.
Sensors (Basel). 2022 Jan 4;22(1):348. doi: 10.3390/s22010348.
Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily "fooled" by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today's demanding security systems-mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC's hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.
生物识别系统是现代安全的基本组成部分。然而，传统的生物识别方法不容易应对其固有的安全缺陷，因为它们可能会受到环境因素的影响，容易被人工复制品“愚弄”等等。这促使研究人员探索其他模态，特别是基于生理信号的模态。心电图 (ECG) 越来越受到关注，近年来已经提出了许多基于 ECG 的安全识别设备，因为心电图信号对于当今要求苛刻的安全系统来说是一个非常有吸引力的解决方案，主要是因为其固有的活体检测优势。这些基于心电图 (ECG) 的设备通常需要满足小尺寸、低吞吐量和功耗限制（例如，电池供电），因此需要具有资源和能源效率。然而，到目前为止，人们对计算性能的关注很少，特别是针对在资源有限的设备中进行边缘处理的部署。因此，这项工作提出了一种基于人工智能 (AI) 的 ECG 识别嵌入式系统的实现，该系统由基于 RISC-V 的片上系统 (SoC) 组成。在我们的 SoC 的硬件加速器中实现了二进制卷积神经网络 (BCNN)，与我们网络的传统非二进制卷积神经网络 (CNN) 版本的软件实现相比，它实现了 176,270 倍的加速，据称优于所有当前基于 CNN 的 ECG 识别方法。
