SEI: Hierarchical Dependency Graphs for Col-Space Design with Application to Leukocyte Detection and Tracking

视频显微镜数据的自动采集和分析有两个障碍。算法变得更加复杂，当前的实现不能进行实时分析，这需要比当前的实现加速三到四个数量级。传统的设计方法本质上限制了它所能生产的系统的质量，限制了工程师和应用专家的能力。该项目在协作空间中共同优化算法和定制系统架构，在协作空间中，可以在考虑算法更改的同时考虑实施更改，从而提高性能。该系统使用层次依赖图作为公共语言来建立AND算法与实现之间的双向关系。来自设计、生物医学图像分析、计算机体系结构和医学的跨学科团队正在开发体内白细胞检测和跟踪的原型系统，并建立协同空间优化设计。这项研究将促进一种新的方法和设计基础设施，以设计将在系统效率和速度方面有显著改进的系统。这项工作将公开发布，不仅会对学生进行培训，还会为算法和实现的设计提供一种新的协作方法。

There are two barriers to the automatic data collection and analysis from video microscopy. The algorithms grow more sophisticated and the current implementations fall short of real-time analysis, which requires a speedup of three or four orders of magnitude over current implementations. The traditional design methodology is inherently limited in the quality of systems it can produce, restricting the capabilities of both the engineers and the application experts. This projects co-optimizes both algorithms and custom system architectures in a collaborative space, where changes in implementation can be considered at the same time as changes in the algorithms, leading to enhanced performance. The system uses hierarchical dependency graphs as a common language to establish the bi-directional relationship between and algorithm and an implementation. The interdisciplinary team from design, biomedical image analysis, computer architecture and medicine, is developing a prototype system for in vivo leukocyte detection and tracking and establishment of a collaborative space optimization design. This research will facilitate a new approach and design infrastructure for designing systems that will have dramatic improvements in system efficiency and speeds. The work will be publicly released and lead not only to training of students but a new collaborative approach to design of algorithms and implementations.