Accelerator-Rich Architectures with Applications to Healthcare

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Many healthcare applications present significant computational challenges. For example, the computational demand for personalized cancer treatment is prohibitively high for the general-purpose computing technologies, as tumor heterogeneity requires great sequencing depths, structural aberrations are difficult to detect with today's methods, and the tumor has the ability to evolve i.e., the same tumor might be assayed a great many times during the course of treatment. The goal of this project is to apply the domain-specific customized computing techniques developed by the Center for Domain-Specific Computing (CDSC) at UCLA to greatly accelerate computation for some key healthcare applications. The CDSC, established in 2009 with the support of the NSF, looks beyond parallelization, and focuses on domain-specific customization as the next disruptive technology for power-performance efficiency improvement. In the past four years, CDSC has demonstrated significant performance and energy efficiency with innovation in developing customizable heterogeneous computing technologies. The current proposal under the NSF Innovation Transition program leverages the research results from CDSC, and focuses on key research problems and solutions to make domain-specific customizable computing feasible and practical for innovation transition to the industry, Specifically, the project will develop accelerator-rich architectures along with unified adaptive runtime systems for personalized cancer treatment, medical image processing, and will enable deployment in several energy efficient programmable platforms capable of handling huge volumes of state of the art real time patient data. The center will continue its already successful outreach program, through a partnership with the UCLA Center for Excellence in Engineering and Diversity, to involve highly diversified high school and undergraduate students for summer research. The success of our project will enable significant advances in medical imaging analysis and personalized cancer treatment, which will greatly improve healthcare quality while reducing cost. The participation of the industrial partner in this InTrans project will greatly facilitate the innovation transition of research results from this project to industry for energy-efficient computing.