Filters








59 Hits in 6.1 sec

Table of contents

2019 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
Koh 1161 Verification Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Babacan, and F. Kaçar 1123 Hardware-Assisted Cross-Generation Prediction of GPUs Under Design . . . . K. O'Neal, P. Brisk, E. Shriver, and M.  ... 
doi:10.1109/tcad.2019.2914133 fatcat:2cvhvyu52bdbfj34wgv66vh7ie

Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning [article]

Haoyu Yang, Shuhe Li, Cyrus Tabery, Bingqing Lin, Bei Yu
2018 arXiv   pre-print
Recent researches try to facilitate the procedure with a reduced flow including feature extraction, training set generation and hotspot detection, where feature extraction methods and hotspot detection  ...  In this paper, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine learning model and the training set that aims to achieve  ...  Given a layout design, the objective of PSHD is sampling representative clips that will generalize the hotspot pattern space and maximize the machine learning model generality, i.e. , maximizing the detection  ... 
arXiv:1807.06446v1 fatcat:fygdxals3rhjxmsefh2rlu4mua

VLSI Mask Optimization: From Shallow To Deep Learning [article]

Haoyu Yang, Wei Zhong, Yuzhe Ma, Hao Geng, Ran Chen, Wanli Chen, Bei Yu
2019 arXiv   pre-print
Recent researches have shown prominent advantages of machine learning techniques dealing with complicated and big data problems, which bring potential of dedicated machine learning solution for DFM problems  ...  In this paper, we focus on a heterogeneous OPC framework that assists mask layout optimization.  ...  [3] consider the limitation of conventional machine learning on scalability requirements for printability estimation and feature representation, a novel deep learning based hotspot detection model is  ... 
arXiv:1912.07254v1 fatcat:x7gt5oobbvb3jhmujrgn6xmxju

On Improving Hotspot Detection Through Synthetic Pattern-Based Database Enhancement [article]

Gaurav Rajavendra Reddy, Constantinos Xanthopoulos, Yiorgos Makris
2020 arXiv   pre-print
The majority of these efforts use either Machine Learning (ML) or Pattern Matching (PM) techniques to identify and predict hotspots in new incoming designs.  ...  One such issue, stemming from complex interaction between design and process, is the problem of design hotspots.  ...  Authors of [14] proposed the use of feature tensors, which retain spatial relationships between features, along with biased learning and batch biased learning [15] .  ... 
arXiv:2007.05879v1 fatcat:agshhguy5fc7porsgnu5ctara4

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  Appendix A: Literature selection Pubmed was searched for papers containing "convolutional" OR "deep learning" in any field.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques

Wajiha Safat, Soahail Asghar, Saira Andleeb Gillani
2021 IEEE Access  
Different Python libraries were applied including Keras with Tensor Flow, Sk Learn, Pandas, Numpy, Seaburn, Scipy, and many others to generate the results. A.  ...  Therefore, the bootstrap random sampling method as shown in Fig. 3 ; an over feature selection method, which is also common since it is the least biased method to generate estimates of population parameters  ... 
doi:10.1109/access.2021.3078117 fatcat:lyn27akrjreprljivq6jqggy2a

Egocentric Activity Recognition and Localization on a 3D Map [article]

Miao Liu, Lingni Ma, Kiran Somasundaram, Yin Li, Kristen Grauman, James M. Rehg, Chao Li
2021 arXiv   pre-print
We address this challenging problem of jointly recognizing and localizing actions of a mobile user on a known 3D map from egocentric videos. To this end, we propose a novel deep probabilistic model.  ...  Our method demonstrates strong results on both action recognition and 3D action localization across seen and unseen environments.  ...  Local environment features and video features are further fused with the video features for action recognition.  ... 
arXiv:2105.09544v2 fatcat:vcil5wq36bavzbne6hmfysb26a

An anticipation experiment for plate tectonics

Tom Gillooly, Nicolas Coltice, Christian Wolf
2019 Tectonics  
However, the fixity assumption is controversial, since mantle convection models with such features identify a slow but significant motion of the deep mantle ponds (Tan et al., 2011) .  ...  To extract knowledge from it, we develop a machine learning framework based on Generative Adversarial Networks (GANs) that learns the regularities of the self-organization in order to fill gaps of observations  ...  Acknowledgments We thank Dietmar Müller, Laurent Montesi, and Thomas Bodin for their fruitful reviews and John Geissman for his patience. We thank Barbara Romanowicz, Anny  ... 
doi:10.1029/2018tc005427 fatcat:dq2bgub235ctvjqpo3eia6gwqe

A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer [article]

Merima Kulin, Tarik Kazaz, Ingrid Moerman, Eli de Poorter
2020 arXiv   pre-print
First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand  ...  This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering  ...  Transfer learning With typical supervised learning a learned model is applicable for a specific scenario and likely biased to the training dataset.  ... 
arXiv:2001.04561v2 fatcat:kbbvgechmjgwla6noolrf6ds7u

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Merima Kulin, Tarik Kazaz, Eli De Poorter, Ingrid Moerman
2021 Electronics  
First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand  ...  This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering  ...  Transfer Learning With typical supervised learning a learned model is applicable for a specific scenario and likely biased to the training dataset.  ... 
doi:10.3390/electronics10030318 fatcat:p6jslz26dvfvbpnqzmrpptloim

Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network [article]

Feng Xiao, Dapeng Zhang, Gang Kou, Lu Li
2020 arXiv   pre-print
To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results.  ...  And the locally connected layers play an important role in dealing with the local statistical differences and activating useful regions.  ...  The work described in this paper was supported by the National Natural Science Foundation of China (71622007, 71861167001) and the National Key Research and Development Program of China (2018YFB1600902  ... 
arXiv:1904.06823v2 fatcat:a5tcgmj3urbb3pjku3mv4q53iy

Feature Extraction and Representation of Urban Road Networks Based on Travel Routes

Shichen Huang, Chunfu Shao, Juan Li, Xiong Yang, Xiaoyu Zhang, Jianpei Qian, Shengyou Wang
2020 Sustainability  
Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model  ...  Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper.  ...  Convolutional neural network (CNN) is a high accuracy deep learning model for feature extraction and classification.  ... 
doi:10.3390/su12229621 fatcat:3hq5jnpmy5gvnmko72seyy6qty

Visual Analytics for Decision Support: A Supply Chain Perspective

Alireza Khakpour, Ricardo Colomo-Palacios, Antonio Martini
2021 IEEE Access  
Finally, the application of different machine learning methods based on different analysis tasks and providing the users with the ability of choosing the underlying machine learning method for analysis  ...  customers moving history in the store Map and Feature visualization Manual Analysis S17 [51] Deliver Detecting changes in customer behavior during a sales campaign.  ... 
doi:10.1109/access.2021.3085496 fatcat:lreijdtcango5khkbglblr7cwq

Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks

Xuhan Guo, Jinlong Xiang, Yujia Zhang, Yikai Su
2021 Advanced Photonics Research  
(a) Reproduced with permission. [40]  ...  Brain-inspired photonic neuromorphic computing for artificial intelligence is raising an urgent need, and it promises orders-ofmagnitude higher computing speed and energy efficiency compared with digital  ...  Acknowledgements X.G. and J. X. contributed equally to this work.  ... 
doi:10.1002/adpr.202000212 fatcat:gdnqg5hzjbaoxonmikfm3qrska

On the Confidence in Bit-Alias Measurement of Physical Unclonable Functions

Florian Wilde, Michael Pehl
2019 2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)  
The proposed methods are publicly available and should improve the design and evaluation of PUFs in the future.  ...  Physical Unclonable Functions (PUFs) are modern solutions for cheap and secure key storage.  ...  For the high-performance functions, we illustrate how the MPPA3 processor accelerates deep learning inference by distributing computations across compute units and cores, and by offloading tensor  ... 
doi:10.1109/newcas44328.2019.8961298 dblp:conf/newcas/WildeP19 fatcat:wv67uzuqlvcmhma3nahzdrr2ta
« Previous Showing results 1 — 15 out of 59 results