Dynamic IR-drop ECO optimization by cell movement with current waveform staggering and machine learning guidance

2020 ◽  
Author(s):  
Xuan-Xue Huang ◽  
Hsien-Chia Chen ◽  
Sheng-Wei Wang ◽  
Iris Hui-Ru Jiang ◽  
Yih-Chih Chou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Movement ◽  
Current Waveform ◽  
Ir Drop

scholarly journals Fast Dynamic IR-Drop Prediction Using Machine Learning in Bulk FinFET Technologies

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1807
Author(s):  
Pengcheng Huang ◽  
Chiyuan Ma ◽  
Zhenyu Wu
Keyword(s):  
Machine Learning ◽  
Integrated Circuits ◽  
Correlation Coefficient ◽  
Prediction Method ◽  
Average Error ◽  
Ir Drop ◽  
Long Time ◽  
Change Order ◽  
Almost All ◽  
Fast Dynamic

IR-drop is a fundamental constraint by almost all integrated circuits (ICs) physical designs, and many iterations of timing engineer change order (ECO), IR-drop ECO, or other ECO are needed before design signoff. However, IR-drop analysis usually takes a long time and wastes so many resources. In this work, we develop a fast dynamic IR-drop predictor based on a machine learning technique, XGBoost, and the prediction method can be applied to vector-based and vectorless IR-drop analysis simultaneously. Correlation coefficient is often used to characterize the symmetry of prediction data and golden data, and our experiments show that the prediction correlation coefficient is more than 0.96 and the average error is no more than 1.3 mV for two industry designs, which are of 2.4 million and 3.7 million instances, respectively, and that the analysis is speeded up over 4.3 times compared with the IR-drop analysis by commercial tool, Redhawk.

Automatic IR-Drop ECO Using Machine Learning

2020 ◽  
Author(s):  
Heng-Yi Lin ◽  
Yen-Chun Fang ◽  
Shi-Tang Liu ◽  
Jia-Xian Chen ◽  
Chien-Mo Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ir Drop

IR drop prediction of ECO-revised circuits using machine learning

2018 ◽  
Author(s):  
Shih-Yao Lin ◽  
Yen-Chun Fang ◽  
Yu-Ching Li ◽  
Yu-Cheng Liu ◽  
Tsung-Shan Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ir Drop

scholarly journals Fast IR drop estimation with machine learning

2020 ◽  
Author(s):  
Zhiyao Xie ◽  
Hai Li ◽  
Xiaoqing Xu ◽  
Jiang Hu ◽  
Yiran Chen
Keyword(s):  
Machine Learning ◽  
Ir Drop

Machine-learning-based dynamic IR drop prediction for ECO

2018 ◽  
Author(s):  
Yen-Chun Fang ◽  
Heng-Yi Lin ◽  
Min-Yan Su ◽  
Chien-Mo Li ◽  
Eric Jia-Wei Fang
Keyword(s):  
Machine Learning ◽  
Ir Drop

scholarly journals OrganoidTracker: efficient cell tracking using machine learning and manual error correction

2020 ◽  
Author(s):  
Rutger N.U. Kok ◽  
Laetitia Hebert ◽  
Guizela Huelsz-Prince ◽  
Yvonne J. Goos ◽  
Xuan Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Tracking ◽  
Cell Movement ◽  
Warning System ◽  
Time Lapse ◽  
Flow Solver ◽  
Machine Learning Approach ◽  
Lineage Trees ◽  
Min Cost Flow ◽  
Cell Trajectories

AbstractTime-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Correlated Studies of Cellular Interactions Using TEM, Freeze Etching, and SEM

1974 ◽  
Vol 32 ◽  
pp. 320-321
Author(s):  
J. P. Revel
Keyword(s):  
Developmental Stages ◽  
Three Dimensional ◽  
Cell Movement ◽  
Cellular Interactions ◽  
Serial Sections ◽  
Cell Sheets ◽  
Freeze Etching ◽  
Early Developmental

Movement of individual cells or of cell sheets and complex patterns of folding play a prominent role in the early developmental stages of the embryo. Our understanding of these processes is based on three- dimensional reconstructions laboriously prepared from serial sections, and from autoradiographic and other studies. Many concepts have also evolved from extrapolation of investigations of cell movement carried out in vitro. The scanning electron microscope now allows us to examine some of these events in situ. It is possible to prepare dissections of embryos and even of tissues of adult animals which reveal existing relationships between various structures more readily than used to be possible vithout an SEM.

Quantitative Freeze Fracture Studies of Gap Junctions in Somatic Cell Hybrids, Their Parents, and Revertants

1976 ◽  
Vol 34 ◽  
pp. 218-219
Author(s):  
W. J. Larsen ◽  
R. Azarnia ◽  
W. R. Loewenstein
Keyword(s):  
Gap Junctions ◽  
Striated Muscle ◽  
Freeze Fracture ◽  
Cell Movement ◽  
Dye Molecules ◽  
Somatic Cell Hybrids ◽  
Cell Coupling ◽  
Normal Cells ◽  
Mediate Cell ◽  
Almost All

Although the physiological significance of the gap junction remains unspecified, these membrane specializations are now recognized as common to almost all normal cells (excluding adult striated muscle and some nerve cells) and are found in organisms ranging from the coelenterates to man. Since it appears likely that these structures mediate the cell-to-cell movement of ions and small dye molecules in some electrical tissues, we undertook this study with the objective of determining whether gap junctions in inexcitable tissues also mediate cell-to-cell coupling.To test this hypothesis, a coupling, human Lesh-Nyhan (LN) cell was fused with a non-coupling, mouse cl-1D cell, and the hybrids, revertants, and parental cells were analysed for coupling with respect both to ions and fluorescein and for membrane junctions with the freeze fracture technique.

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