CAREER: AI-Enabled Self-Healing and Trusted Wireless Transceivers for Biomedical Applications

在后COVID-19时代，医疗界越来越多地采用远程医疗作为传统医学的替代方案。无线连接的生物医学设备是这种远程医疗解决方案不可或缺的一部分。随着患者寿命的延长，生物医学设备中使用的无线收发器的长期可靠性正成为一个问题。此外，个性化医疗保健的敏感性引起了对数据安全性的担忧。该CAREER项目将研究射频集成电路（RFIC）中的安全威胁和故障机制，并开发智能，低能耗的模拟解决方案，以创建具有检测和修复损伤能力的可信无线收发器。该CAREER项目中的拟议研究将从根本上改变远程医疗保健解决方案，开发一种新型的小型化自愈和可信无线收发器，可以在设备级提供高速连接。此外，该项目通过引入用于安全数据通信的新型低能量模拟加密技术来增强硬件安全的基础知识。该项目的教育计划将大大提高学生在通信和硬件安全方面的知识。通过与工业界的合作，学生将有机会与工业导师合作，并获得电子方面的实用知识。该计划还包含重点关注K-12 STEM教育的举措。作为这一努力的一部分，将为当地高中学生及其教师提供安全电子和数据通信暑期讲习班，然后举办设计比赛，以激励学生，特别是那些来自代表性不足的群体和少数民族的学生，寻求STEM相关领域的中学后教育。此外，我们亦会举办实验室参观及靴子训练营，作为外展活动的一部分，以分享资源及促进知识转移给教师及学生。这个CAREER项目的目标是透过引入低能量模拟非对称加密及自适应自愈技术，开发智能、自愈及可信的无线收发器。拟议的研究包括两个研究重点领域：（1）具有智能威胁检测能力的低能耗可信数据通信，以及（2）智能自愈。这两个研究方向都受益于节能模拟神经网络（ANN），以改善功能。在无线收发器中的调制波形上应用创新的非对称模拟加密，创建了节能的端到端加密无线通信链路。所提出的设计将使用可扩展线性时不变（LTI）来生成加密和解密所需的密钥。低能量ANN将监视收发器参数以发现任何潜在攻击的迹象，并相应地通知加密引擎。收发器还将使用低开销设备指纹技术进行认证。类似地，将开发低能量自适应模拟自愈单元，以通过检测性能退化和异常来增加可靠性，并主动调整收发器参数。自愈单元采用创新的双环自适应结构。第一个环路内置于模拟前端（AFE）中，始终用于监控短期性能下降，而第二个环路依赖于低能量ANN，并选择性地对数据进行采样，以纠正长期缺陷。在这两个研究中使用的人工神经网络都是使用节能尖峰神经网络（SNN）构建的，并且是共同设计的，以减少延迟和面积开销。该CAREER项目的成果将通过加速医疗界采用智能个性化解决方案来加强远程医疗，特别是慢性病的长期治疗。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In the post COVID-19 era, the medical community is increasingly adopting remote healthcare as an alternative to conventional medicine. Wirelessly-connected biomedical devices are an indispensable part of such remote healthcare solutions. With the increased longevity of patients, the long-term reliability of the wireless transceivers used in biomedical devices is becoming a concern. Furthermore, the sensitive nature of personalized healthcare raises concerns about the data security. This CAREER project will study the security threats and failure mechanisms in radio frequency integrated circuits (RFIC) and develop intelligent, low-energy analog solutions so as to create a trusted wireless transceiver with the capability to detect and cure impairments. The proposed research in this CAREER project will fundamentally change the remote healthcare solutions by developing a new class of miniaturized self-healing and trusted wireless transceivers that can provide high-speed connectivity at the device level. Moreover, this project enhances the foundational knowledge in hardware security by introducing novel low-energy analog encryption techniques for use in secure data communications. The education plan in this project will significantly enhance the knowledge of students in communications and hardware security. Through collaboration with industry, students will have the opportunity to work with industrial mentors and gain practical knowledge in electronics. The plan also contains initiatives focused on STEM education in K-12. As part of this effort, summer workshops on secure electronics and data communications will be offered to local high school students and their teachers, followed by design competitions to inspire the students, particularly those from underrepresented groups and minorities, to seek post-secondary education in STEM related fields. Lab visits and boot camps will also be organized as part of outreach activities to share resources and facilitate the knowledge transfer to teachers and students.The goal of this CAREER project is to develop intelligent, self-healing and trusted wireless transceivers by introducing low-energy analog asymmetric encryption and adaptive self-healing. The proposed research consist of two research thrust areas: (1) low-energy trusted data communications with smart threat detection capability, and (2) intelligent self-healing. Both research thrusts benefit from energy-efficient analog neural networks (ANNs) to improve the functionality. Applying innovative asymmetric analog encryption on the modulated waveforms in the wireless transceiver, an energy-efficient end-to-end encrypted wireless communication link is created. The proposed design will use scalable linear time-invariant (LTI) to generate the keys needed for encryption and decryption. A low-energy ANN will monitor the transceiver parameters for any sign of potential attack and notify the encryption engine accordingly. The transceiver will also be authenticated using low-overhead device fingerprinting techniques. Similarly, a low-energy adaptive analog self-healing unit will be developed to increase the reliability by detecting the performance degradation and abnormalities, and actively adjust the transceiver parameters. The self-healing unit uses an innovative dual-loop adaptive structure. The first loop is built within the analog front-end (AFE) and is always engaged to monitor short-term performance degradations while the second loop relies on a low-energy ANN and samples the data selectively to correct for long-term defects. The ANNs used in both research thrusts are built using energy-efficient spiking neural networks (SNNs) and are co-designed to reduce the delay and area overhead. The outcome of this CAREER project will enhance the remote healthcare by accelerating the adoption of smart personalized solutions in medical community, particularly for long-term treatment of chronic diseases. It will also lay the foundation of a smart, self-healing and trusted electronic platform for biomedical applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.