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

随着小型、低成本的可穿戴计算技术的快速发展，开发能够持续警惕地监测生理信号的个人健康监测设备的机会很大。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，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能够快速准确地处理和解释大量数据，并仅在必要时生成智能警报。该项目的目标是建立下一代异构生物医学信号处理平台的基础，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性，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多核加速器，并将其与现成的嵌入式处理器集成，这些处理器将这些应用程序的性能、可扩展性、可编程性和功率效率要求结合在一起。这些pi将在硬件上实现所提出的异构架构，并将在许多现实生物医学工作负载中评估其性能和功率效率，包括癫痫检测、手持超声光谱多普勒和成像、舌驱动辅助装置和假手控制接口。拟议的跨学科研究工作可以激发和实现医疗保健监测的新方法，并可以显著影响几个领域，包括以人为中心的网络物理系统、网络安全、移动通信、生物信息学和需要来自不同传感器的高性能和节能嵌入式计算的应用。所提出的基准、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。研究成果将整合到两个校区的研究人员开设的研究生和本科课程中。pi活跃在几个校园范围内和全国性的组织中，这些组织致力于吸引和留住代表性不足的群体成员从事研究并完成科学和工程研究生学位。

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.