CSR: Small:Collaborative Research:Heterogeneous Ultra Low Power Accelerator for Wearable Biomedical Computing

CSR：小型：协作研究：用于可穿戴生物医学计算的异构超低功耗加速器

• 批准号: 1527151
• 负责人: Tinoosh Mohsenin
• 金额: $ 21.2万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527151&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Heterogeneous

With the rapid advances in small, low-cost wearable computing technologies, there is a tremendous opportunity to develop personal health monitoring devices capable of continuous vigilant monitoring of physiological signals. Wearable biomedical devices have the potential to reduce the morbidity, mortality, and economic cost associated with many chronic diseases by enabling early intervention and preventing costly hospitalizations. These low power systems require to have the capacity to provide fast and accurate processing and interpretation of vast amounts of data and generate smart alarms only when warranted. The objective of this project is to build the foundation of the next generation of heterogeneous biomedical signal processing platforms that can address the current and future generation energy-efficiency requirements and computational demands. The PIs start with understanding the specific characteristics of emerging biomedical signal and imaging applications on off-the-shelf embedded low power multicore CPU, GPU and FPGA platforms to accurately understand the trade-offs they offer and the bottlenecks they have. Based on these results, the PIs will design and architect a domain-specific manycore accelerator in hardware and integrate it with an off-the-shelf embedded processor that together combine performance, scalability, programmability, and power efficiency requirements for these applications. The PIs will implement the proposed heterogeneous architecture in hardware and will evaluate its performance and power efficiency with a number of real-life biomedical workloads including seizure detection, handheld ultrasound spectral Doppler and imaging, tongue drive assistive device and prosthetic hand control interface.The proposed interdisciplinary research effort could inspire and enable new approaches to healthcare monitoring, and can significantly impact several fields including human-centered cyber-physical systems, cyber-security, mobile communications, bioinformatics and applications that require high performance and energy efficient embedded computing from different sensors. The proposed benchmark, characterization, and software-hardware computing framework will be freely shared and broadly disseminated among colleagues in related disciplines. Research results will be integrated in graduate and undergraduate courses offered by the investigators in both campuses. The PIs are active in several campus-wide and national organizations that work to attract and retain members of under-represented groups to engage in research and complete graduate degrees in science and engineering.

随着小型、低成本可穿戴计算技术的快速发展，存在开发能够连续警惕地监测生理信号的个人健康监测设备的巨大机会。可穿戴生物医学设备有可能通过早期干预和防止昂贵的住院治疗来降低与许多慢性疾病相关的发病率、死亡率和经济成本。这些低功耗系统需要有能力对大量数据进行快速准确的处理和解释，并仅在必要时生成智能警报。该项目的目标是为下一代异构生物医学信号处理平台奠定基础，以满足当前和未来一代的能效要求和计算需求。PI首先要了解现有嵌入式低功耗多核CPU、GPU和FPGA平台上新兴生物医学信号和成像应用的具体特征，以准确了解它们提供的权衡和瓶颈。基于这些结果，PI将在硬件中设计和构建特定领域的众核加速器，并将其与现成的嵌入式处理器集成，这些处理器将联合收割机的性能、可扩展性、可编程性和能效要求结合在一起，以满足这些应用的要求。研究人员将在硬件中实现拟议的异构架构，并将评估其性能和功率效率与一些现实生活中的生物医学工作负载，包括癫痫发作检测，手持超声频谱多普勒和成像，舌头驱动辅助设备和假肢手控制界面。拟议的跨学科研究工作可能会激发和实现新的医疗监测方法，并且可以显著地影响若干领域，包括以人为中心的网络物理系统、网络安全、移动的通信、生物信息学以及需要来自不同传感器的高性能和高能效嵌入式计算的应用。拟议的基准、表征和软硬件计算框架将在相关学科的同事之间自由共享和广泛传播。 研究结果将被整合到研究人员在两个校区提供的研究生和本科生课程中。PI活跃于几个校园范围和国家组织，这些组织致力于吸引和留住代表性不足的群体的成员，以从事研究并完成科学和工程研究生学位。