Accelerator-Rich Architectures with Applications to Healthcare

许多医疗保健应用提出了重大的计算挑战。例如，个性化癌症治疗的计算需求对于通用计算技术而言过高，因为肿瘤异质性需要很大的测序深度，结构畸变难以用当今的方法检测，并且肿瘤具有进化的能力，即，同一肿瘤在治疗过程中可能被检测很多次。该项目的目标是应用加州大学洛杉矶分校特定领域计算中心（CDSC）开发的特定领域定制计算技术，大大加快一些关键医疗应用的计算速度。CDSC于2009年在NSF的支持下成立，它超越了并行化，专注于特定领域的定制，将其作为下一个突破性技术，以提高电源性能效率。在过去的四年里，CDSC在开发可定制的异构计算技术方面的创新表现出了显著的性能和能源效率。NSF创新过渡计划下的当前提案利用了CDSC的研究成果，并专注于关键研究问题和解决方案，以使特定领域的可定制计算对于创新过渡到行业变得可行和实用，具体而言，该项目将开发加速器丰富的架构沿着统一的自适应运行时系统，用于个性化癌症治疗，医学图像处理，并且将使得能够在能够处理大量现有技术的真实的时间患者数据的多个能量有效的可编程平台中进行部署。该中心将继续其已经成功的推广计划，通过与加州大学洛杉矶分校卓越工程和多样性中心的合作伙伴关系，让高度多样化的高中和本科生参与暑期研究。我们项目的成功将使医学成像分析和个性化癌症治疗取得重大进展，这将大大提高医疗质量，同时降低成本。工业合作伙伴参与该InTrans项目将极大地促进该项目研究成果向工业节能计算的创新过渡。

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.