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

从视频显微镜中自动采集和分析数据有两个障碍。算法变得越来越复杂，当前的实现无法进行实时分析，这需要比当前实现提高三到四个数量级的速度。传统的设计方法在其所能产生的系统质量上存在固有的局限性，从而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自定义系统架构，其中实现的更改可以与算法的更改同时考虑，从而提高性能。该系统使用分层依赖图作为公共语言，建立算法与实现之间的双向关系。来自设计、生物医学图像分析、计算机架构和医学的跨学科团队正在开发一个用于体内白细胞检测和跟踪的原型系统，并建立一个协同空间优化设计。这项研究将为设计系统提供一种新的方法和设计基础设施，从而大大提高系统的效率和速度。这项工作将被公开发布，不仅会对学生进行培训，还会为算法设计和实现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.