Green Computing Innovations for Biomedical Imaging Applications

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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.