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    • 4. 发明授权
    • Placement method for integrated circuit design using topo-clustering
    • 使用拓扑聚类的集成电路设计的放置方法
    • US06851099B1
    • 2005-02-01
    • US09511705
    • 2000-02-23
    • Majid SarrafzadehSalil R. Raje
    • Majid SarrafzadehSalil R. Raje
    • G06F17/50
    • G06F17/5072
    • The present invention, generally speaking, provides a placement method for the physical design of integrated circuits in which natural topological feature clusters (topo-clusters) are discovered and exploited during the placement process. Initial placement and placement refinement may be performed hierarchically using topocluster trees. A topocluster tree may be used to drive initial placement. An iterative placement refinement process then follows, using a technique referred to herein as Geometrically-Bounded FM (GBFM). In GBFM, FM is applied on a local basis to windows encompassing some number of bins. From iteration to iteration, windows may shift position and vary in size. When a region bounded by a window meets a specified cost threshold in terms of a specified cost function, that region does not participate. The cost function takes account of actual physical metrics-delay, area, congestion, power, etc. During placement refinement using GBFM, cluster size is adjusted iteratively from large to small as determined by horizontal cuts within the topocluster tree. GBFM occurs in the context of recursive quadrisection. Hence, after GBFM has been completed, a further quadrisection step is performed in which each bin is divided into four bins, with a quarter of the gates of the original bin being placed in the center of each of the resulting bins. GBFM then follows, and the cycle repeats until each bin contains a fairly small number of gates. Topocluster trees may also be used for quadrisection. Following the foregoing global placement process, the circuit is then ready for detailed placement in which cells are assigned to placement rows.
    • 本发明一般地提供了一种用于集成电路的物理设计的放置方法,其中在放置过程中发现和利用了自然拓扑特征簇(地形簇)。 初始放置和放置细化可以使用顶簇树进行分层执行。 可以使用顶层树来驱动初始放置。 然后,使用本文称为几何有界FM(GBFM)的技术,随后进行迭代放置细化过程。 在GBFM中,FM在本地基础上应用于包含一些数量的分区的窗口。 从迭代到迭代,窗口可能会改变位置并且大小变化。 当由窗口界定的区域在指定的成本函数方面满足指定的成本阈值时,该区域不参与。 成本函数考虑了实际的物理量度 - 延迟,区域,拥塞,功率等。在使用GBFM进行布局优化时,通过顶层树中的水平切割确定了簇大小从大到小的迭代调整。 GBFM发生在递归四分法的上下文中。 因此,在GBFM完成之后,执行进一步的四分法步骤,其中每个仓分为四个仓,原始仓的四分之一的门被放置在每个结果仓的中心。 然后遵循GBFM,循环重复,直到每个仓包含相当少数量的门。 群集树也可用于四次检验。 按照上述全局放置过程,电路准备好进行详细的放置,其中将单元格分配给放置行。
    • 8. 发明申请
    • SYSTEMS AND METHODS FOR MISSING DATA IMPUTATION
    • 用于丢失数据传输的系统和方法
    • US20140207493A1
    • 2014-07-24
    • US14241431
    • 2012-08-27
    • Majid SarrafzadehMyung-kyung Suh
    • Majid SarrafzadehMyung-kyung Suh
    • G06F19/00
    • G16H10/60A61B5/7264A61B5/7267G06F19/3418G06N7/005G16H50/20
    • Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the system's effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. Embodiments of the present disclosure may utilize machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall.
    • 充血性心力衰竭(CHF)是美国的主要死亡原因。 WANDA是一个利用传感器技术和无线通信监测CHF患者健康状况的无线健康项目。 WANDA的首例试验研究显示,该系统对CHF患者的疗效。 然而,由于系统误用,不用和故障,WANDA遭遇了大量的丢失数据。 缺少数据是非常不希望的,因为自动化警报可能无法通知医疗保健专业人员潜在的危险患者状况。 本公开的实施例可以利用机器学习技术,包括通过贡献估计回归(PACE)的投影调整,贝叶斯方法和投票特征区间(VFI)算法来预测非二项和二项数据。 实验结果表明,上述算法优于其他具有高精度和召回率的方法。