@inproceedings {Rhodes Island, Greece, title = {Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach}, year = {2023}, month = {06/2023}, address = {Rhodes Island, Greece}, keywords = {psclab}, author = {Zhikang Zhang and Bruno Machado Trindade and Michael Green and Zifan Yu and Christopher Pawlowicz and Fengbo Ren} } @inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @article {379, title = {A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing}, journal = {IEEE Transactions on Industrial Electronics (TIE)}, volume = {66}, year = {2019}, month = {10/2018}, pages = {7407-7416}, abstract = {

The scene text interpretation is a critical part of natural scene interpretation. Currently, most of the existing work is based on high-end GPU implementation, which is commonly used on the server side. However, in IoT application scenarios, the communication overhead from the edge device to the server is quite large, which sometimes even dominates the total processing time. Hence, the edgecomputing oriented design is needed to solve this problem. In this paper, we present an architectural design and implementation of a natural scene text interpretation (NSTI) accelerator, which can classify and localize the text region on pixel-level efficiently in real-time on mobile devices. To target the real-time and low-latency processing, the Binary Convolutional Encoder-decoder Network (B-CEDNet) is adopted as the core architecture to enable massive parallelism due to its binary feature. Massively parallelized computations and a highly pipelined data flow control enhance its latency and throughput performance. In addition, all the binarized intermediate results and parameters are stored on chip to eliminate the power consumption and latency overhead of the off-chip communication. The NSTI accelerator is implemented in a 40nm CMOS technology, which can process scene text images (size of 128x32) at 34 fps and latency of 40 ms for pixelwise interpretation with the pixelwise classification accuracy over 90\% on ICDAR- 03 and ICDAR-13 dataset. The real energy-efficiency is 698 GOP/s/W and the peak energy-efficiency can get up to 7825 GOP/s/W. The proposed accelerator is 7 more energy efficient than its optimized GPU-based implementation counterpart, while maintaining a real-time throughput with latency of 40 ms.

}, keywords = {psclab}, doi = {10.1109/TIE.2018.2875643}, url = {https://ieeexplore.ieee.org/document/8513982}, author = {YIxing Li and Zichuan Liu and Wenye Liu and Yu Jiang and Yongliang Wang and Wang Ling Goh and Hao Yu and Fengbo Ren} } @article {366, title = {A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {16}, year = {2019}, month = {05/2018}, pages = {1794-1801}, abstract = {

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e. the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91\% specificity and 90\% average accuracy over the targeted CEUS images for prostate cancer detection, which was superior (p \< 0.05) than previously reported approaches and implementations.

}, keywords = {psclab}, doi = {10.1109/TCBB.2018.2835444}, url = {https://ieeexplore.ieee.org/document/8357938}, author = {Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo and Fang Tang and Fengbo Ren and Jishun Guo and Shuiwang Ji} } @inproceedings {Orleans, Louisana, title = {SqueezedText: A Real-time Scene Text Recognition by Binary Convolutional Encoder-decoder Network}, year = {2018}, month = {04/2018}, pages = {7194-7201}, address = {New Orleans, Louisana}, abstract = {

A new approach for real-time scene text recognition is proposed in this paper. A novel binary convolutional encoderdecoder network (B-CEDNet) together with a bidirectional recurrent neural network (Bi-RNN). The B-CEDNet is engaged as a visual front-end to provide elaborated character detection, and a back-end Bi-RNN performs characterlevel sequential correction and classification based on learned contextual knowledge. The front-end B-CEDNet can process multiple regions containing characters using a one-off forward operation, and is trained under binary constraints with significant compression. Hence it leads to both remarkable inference run-time speedup as well as memory usage reduction. With the elaborated character detection, the back-end Bi-RNN merely processes a low dimension feature sequence with category and spatial information of extracted characters for sequence correction and classification. By training with over 1,000,000 synthetic scene text images, the B-CEDNet achieves a recall rate of 0.86, precision of 0.88 and F-score of 0.87 on ICDAR-03 and ICDAR-13. With the correction and classification by Bi-RNN, the proposed real-time scene text recognition achieves state-of-the-art accuracy while only consumes less than 1-ms inference run-time. The flow processing flow is realized on GPU with a small network size of 1.01 MB for B-CEDNet and 3.23 MB for Bi-RNN, which is much faster and smaller than the existing solutions.

}, keywords = {psclab}, author = {Zichuan Liu and YIxing Li and Fengbo Ren and Hao Yu and Wangling Goh} } @inproceedings {gupta2015deep, title = {Deep learning with limited numerical precision}, year = {2015}, pages = {1737{\textendash}1746}, keywords = {Deep Learning, ref}, author = {Gupta, Suyog and Agrawal, Ankur and Gopalakrishnan, Kailash and Narayanan, Pritish} } @inproceedings {zhang2015optimizing, title = {Optimizing fpga-based accelerator design for deep convolutional neural networks}, year = {2015}, pages = {161{\textendash}170}, publisher = {ACM}, keywords = {Deep Learning, ref}, author = {Zhang, Chen and Li, Peng and Sun, Guangyu and Guan, Yijin and Xiao, Bingjun and Cong, Jason} } @article {putnam2015reconfigurable, title = {A reconfigurable fabric for accelerating large-scale datacenter services}, journal = {IEEE Micro}, volume = {35}, number = {3}, year = {2015}, pages = {10{\textendash}22}, publisher = {IEEE}, keywords = {Deep Learning, ref}, author = {Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others} } @article {17, title = {Statistical Timing and Power Analysis of VLSI Considering Non-linear Dependence}, journal = {the VLSI Journal Integration}, volume = {47}, year = {2014}, month = {Sep.}, pages = {487{\textendash}498}, abstract = {

Majority of practical multivariate statistical analysis and optimizations model interdependence among random variables in terms of the linear correlation. Though linear correlation is simple to use and evaluate, in several cases non-linear dependence between random variables may be too strong to ignore. In this paper, we propose polynomial correlation coefficients as simple measure of multi-variable non-linear dependence and show that the need for modeling non-linear dependence strongly depends on the end function that is to be evaluated from the random variables. Then, we calculate the errors in estimation resulting from assuming independence of components generated by linear de-correlation techniques, such as PCA and ICA. The experimental results show that the error predicted by our method is within 1\% error compared to the real simulation of statistical timing and leakage analysis. In order to deal with non-linear dependence, we further develop a target-function-driven component analysis algorithm (FCA) to minimize the error caused by ignoring high order dependence. We apply FCA to statistical leakage power analysis and SRAM cell noise margin variation analysis. Experimental results show that the proposed FCA method is more accurate compared to the traditional PCA or ICA.

}, keywords = {psclab}, url = {http://www.sciencedirect.com/science/article/pii/S0167926014000029}, author = {Cheng, Lerong and Xu, Wenyao and Ren, Fengbo and Gong, Fang and Gupta, Puneet and He, Lei} } @article {nam2013cosparse, title = {The cosparse analysis model and algorithms}, journal = {Applied and Computational Harmonic Analysis}, volume = {34}, number = {1}, year = {2013}, pages = {30{\textendash}56}, publisher = {Elsevier}, keywords = {Compressive Sensing, ref}, url = {http://www.small-project.eu/index.html}, author = {Nam, Sangnam and Davies, Mike E and Elad, Michael and Gribonval, R{\'e}mi} } @article {277, title = {MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program}, journal = {Bioinformatics}, volume = {30}, year = {2013}, pages = {439{\textendash}441}, abstract = {

Motivation:\ Molecular dynamics (MD) simulations have had a profound impact on studies of membrane proteins during past two decades, but the accuracy of MD simulations of membranes is limited by the quality of membrane models and the applied force fields. Membrane models used in MD simulations mostly contain one kind of lipid molecule. This is far from reality, for biological membranes always contain more than one kind of lipid molecule. Moreover, the lipid composition and their distribution are functionally important. As a result, there is a necessity to prepare more realistic lipid membranes containing different types of lipids at physiological concentrations.

Results:\ To automate and simplify the building process of heterogeneous lipid bilayers as well as providing molecular topologies for included lipids based on both united and all-atom force fields, we provided MemBuilder as a web-based graphical user interface.

Availability and implementation:\ MemBuilder is a free web server available\ 

}, keywords = {other}, author = {Ghahremanpour, Mohammad Mehdi and Arab, Seyed Shahriar and Biookaghazadeh, Saman and Zhang, Jin and van der Spoel, David} } @inproceedings {30, title = {Proper Running Posture Guide: A Wearable Biomechanics Capture System}, year = {2013}, month = {09/2013}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, abstract = {

Running is a popular exercise for all age groups. It helps heart and lung functions, enhances muscle strength and control weight. Nevertheless, excessive fatigue and severe injury resulting from inappropriate running poses might reduce the benefits brought by this exercise and stop people from keeping running regularly. In this paper, we design a system that can monitor the running biomechanics, infer running poses, analyze running patterns, and provide both real-time and off-line feedbacks to reduce unnecessary fatigue and unwanted injuries. Common inappropriate running patterns, over-striding, over-pronating and out-sync, are identified and analyzed with the continuous wearable sensor data streams. Two types of correctional feedbacks are designed to provide users appropriate adjustment guidance: vibrating the areas of user body where improper poses are detected and visualizing running video with sensor waveforms to indicate which inappropriate motions trigger the vibration. With reasonable adjustment, running can be a safe and effective activity for a healthy lifestyle.

}, keywords = {psclab}, url = {http://dl.acm.org/citation.cfm?id=2555333}, author = {Zhang, Xiaoyi and Huang, Ming-Chun and Ren, Fengbo and Xu, Wenyao and Guan, Nan and Yi, Wang} } @inproceedings {perez2009supervised, title = {A supervised learning method for neural networks based on sensitivity analysis with automatic regularization}, year = {2009}, pages = {157{\textendash}164}, publisher = {Springer}, keywords = {Deep Learning, ref}, author = {P{\'e}rez-S{\'a}nchez, Beatriz and Fontenla-Romero, Oscar and Guijarro-Berdi{\~n}as, Bertha} } @article {tropp2007signal, title = {Signal recovery from random measurements via orthogonal matching pursuit}, journal = {IEEE Transactions on information theory}, volume = {53}, number = {12}, year = {2007}, pages = {4655{\textendash}4666}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Tropp, Joel A and Gilbert, Anna C} }