CRII: FET: Embedded neuromorphic circuits for real-time closed-loop biosensor data processing

CRII：FET：用于实时闭环生物传感器数据处理的嵌入式神经形态电路

• 批准号: 1948127
• 负责人: Gina Adam
• 金额: $ 17.5万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948127&HistoricalAwards=false
• 关键词: CRII; FET; Embedded; neuromorphic; circuits

In the last decade, there has been a revolution in biosensor devices producing a wealth of new data from patients. Increased density of sensors, for example embedded in implantable organ-conformal bioelectronic platforms, can provide high-definition spatial and temporal data for increased accuracy of detection and diagnostics. However, gathering a large quantity of data is insufficient if it is not matched by powerful circuitry to process the data and apply a therapeutic response in sometimes as fast as milliseconds. The next generation of biomedical technologies requires novel computing that can be embedded non-intrusively and can translate large quantities of time-sensitive biomedical data into a life-saving response rapidly and energy-efficiently. Distributed neuromorphic computing can provide faster, lower-power and more compact implementations than conventional computing, especially if implemented with emerging device technologies like resistive switches or memristors. Such neuromorphic chips designed for distributed computing can be co-located with the sensors and actuators for closed-loop diagnostics and therapy. This project will provide the fundamental investigations into the requirements and architecture for such distributed closed-loop computing. This technology will impact research in biomedical engineering, opening the path to computing of large amounts multi-physics data gathered in-vivo from organs and artificial tissue. Long term, this work could benefit healthcare, when embedded neuromorphic chips are integrated in a stand-alone implantable system that can enable real-time diagnostics and painless therapy for patients. These findings are also directly transferable to other applications in need of embedded computing hardware such as microrobots and Internet of Things (IoT).This project will develop a neuromorphic computing solution that can be reliably embedded with existing sensor and actuator organ-conformal platforms. The proposed technology is based on hybrid neuromorphic chips organized in a cellular neural network with recurrence. To demonstrate its potential, the computing platform is be prototyped and tested for the analysis of cardiac wavefronts, which have stringent time constants of milliseconds. Initially, theoretical investigations will focus on developing a hardware-mappable algorithm that can distinguish electrical storms from normal electrical wave patterns. Then, experimental work focuses on taping out a cellular neural network processing unit in a hybrid memristor / transistor (CMOS) technology and testing a small distributed network of such units. This work benefits from the use of cardiac animal and human data for testing, thanks to collaborators in the Biomedical Engineering Department at George Washington University. The prototype demonstration and the supporting simulations serve as hands-on materials in a class on neuromorphic hardware and in various outreach efforts planned as part of the new GWU Center for Women in Engineering. A PhD student is being recruited, and an undergraduate student and a high school student are to be involved in the design and testing of this computing platform.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.

在过去的十年中，生物传感器设备发生了一场革命，从患者那里产生了大量的新数据。例如嵌入在可植入器官适形生物电子平台中的传感器的增加的密度可以提供高清晰度的空间和时间数据，以提高检测和诊断的准确性。然而，如果没有强大的电路来处理数据并在有时快至毫秒的时间内应用治疗反应，则收集大量数据是不够的。下一代生物医学技术需要新的计算，可以非侵入性地嵌入，并可以将大量时间敏感的生物医学数据快速高效地转化为拯救生命的响应。分布式神经形态计算可以提供比传统计算更快，更低功耗和更紧凑的实现，特别是如果使用新兴的设备技术（如电阻开关或忆阻器）实现。这种为分布式计算而设计的神经形态芯片可以与传感器和致动器协同定位，用于闭环诊断和治疗。这个项目将提供这种分布式闭环计算的需求和架构的基本调查。这项技术将影响生物医学工程的研究，为计算从器官和人造组织体内收集的大量多物理数据开辟道路。从长远来看，当嵌入式神经形态芯片集成在一个独立的植入式系统中时，这项工作可能会使医疗保健受益，该系统可以为患者提供实时诊断和无痛治疗。这些发现也可以直接转移到其他需要嵌入式计算硬件的应用中，如微型机器人和物联网（IoT）。该项目将开发一种神经形态计算解决方案，可以可靠地嵌入现有的传感器和执行器器官适形平台。所提出的技术是基于组织在一个细胞神经网络与复发的混合神经形态芯片。为了展示其潜力，计算平台被原型化并测试用于分析心脏波前，其具有毫秒的严格时间常数。最初，理论研究将集中在开发一种硬件映射算法，可以区分电风暴和正常的电波模式。然后，实验工作的重点是在混合忆阻器/晶体管（CMOS）技术中录制出细胞神经网络处理单元，并测试这些单元的小型分布式网络。这项工作得益于使用心脏动物和人类数据进行测试，这要归功于乔治华盛顿大学生物医学工程系的合作者。原型演示和支持模拟作为动手材料在类神经形态硬件和各种推广工作计划作为新的GWU中心的一部分妇女在工程。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Hardware-Mappable Cellular Neural Networks for Distributed Wavefront Detection in Next-Generation Cardiac Implants.

用于下一代心脏植入物中分布式波前检测的硬件可映射细胞神经网络。

• DOI: 10.1002/aisy.202200032
• 发表时间: 2022
• 期刊: Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)
• 作者: Yang,Zhuolin;Zhang,Lei;Aras,Kedar;Efimov,IgorR;Adam,GinaC
• 通讯作者: Adam,GinaC

Discrete Hilbert Transform via Memristor Crossbars for Compact Biosignal Processing

• DOI: 10.1109/aict55583.2022.10013604
• 发表时间: 2022-10
• 期刊: 2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)
• 作者: Lei Zhang;Zhuolin Yang;Kedar K. Aras;Igor R. Efimov;G. Adam