Green Computing Innovations for Biomedical Imaging Applications

生物医学成像应用的绿色计算创新

• 批准号: RGPIN-2020-05416
• 负责人: Rakhmatov, Daler
• 金额: $ 1.75万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717631
• 关键词: Green; Computing; Innovations; Biomedical; Imaging

Our research program aims to advance green embedded computing technologies targeting mobile computation-intensive biomedical imaging applications. Given that such apps run on battery-powered portable devices, embedded system designers and programmers are faced with multiple conflicting requirements, such as high computational throughput, low power consumption, high data quality, and low implementation cost, among others. This technical challenge can be tackled by providing application-specific architectural support for runtime system adaptation, whereby a set of power-managed and flexible hardware/software components (e.g., embedded CPUs, GPUs, FPGAs, specialized processing units, and their programming libraries) are dynamically tuned to enhance system energy efficiency during targeted application execution (e.g., ultrasound image formation). Our short-term objectives are to research and develop: 1) innovative low-complexity algorithms for multidimensional data acquisition, processing, and analysis that specifically target ultrasound imaging applications, and 2) innovative configurable hardware/software architectures that support efficient and flexible execution of these algorithms. Our long-term objectives are to research and develop: 1) predictive system-level models producing battery-aware energy budget estimates to guide application execution under energy constraints, and 2) graph-based algorithms for dynamic hardware/software configuration management to maximize architectural energy efficiency while maintaining a desired application-aware quality of service. Our ultimate goal is to facilitate model-driven design and optimization of embedded system resources, processes, and data, aiming to achieve an effective balance between delivered performance and battery life during execution of targeted biomedical applications. This proposal supports a strong HQP training environment, in which the supervised personnel will gain applicable knowledge and skills cutting across multiple technical subjects, such as ultrasound imaging, signal processing, hardware/software codesign, reconfigurable architectures, energy-efficient computing, system-level modeling, and optimization algorithms. We anticipate that our research findings and trained HQP will be of substantial benefit to Canadian high-tech companies and public-sector organizations developing innovative solutions in the area of mobile embedded computing systems and applications.

我们的研究计划旨在推进绿色嵌入式计算技术，目标是移动计算密集型生物医学成像应用。鉴于此类应用程序运行在电池供电的便携式设备上，嵌入式系统设计人员和程序员面临着多种相互冲突的需求，例如高计算吞吐量、低功耗、高数据质量和低实现成本等。这一技术挑战可以通过为运行时系统适应提供特定于应用程序的架构支持来解决，即一组电源管理和灵活的硬件/软件组件（例如，嵌入式cpu， gpu, fpga，专用处理单元及其编程库）在目标应用程序执行期间动态调整以提高系统能效（例如，超声图像形成）。