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作者： Nejahi, Younes Wayne State University

学位级别：Ph.D.

The increase in CPU cores and the growth of coprocessors and accelerators has given us a great performance advantage over the past few decades. However, to fully utilize these resources, developers face many challenges. In some cases, the problem is inherently serial in nature and dependencies prevent workload to be easily parallelizable. Memory bandwidth between host and accelerators can also become a bottleneck in data-intensive applications. There are methodologies developed by researchers to tackle each of these problems and in this work, we will study two optimization techniques at micro and macro levels. One class of problems is stencil operations which is a data-intensive algorithm. Stencil code is used in numerous areas such as computer simulations, partial differential equations, image processing, and cellular automata. To optimize this algorithm, micro-level optimization was performed to reduce the memory transaction required in the algorithm. NVIDIA warp shuffle technology allows developers to transfer data between threads without requiring to transfer to and from higher memory levels such as shared memory. Another class of problems is force field optimization of Monte Carlo simulation engines. Currently, experts spend months using heuristics and experience to find optimal values for force field parameters. This task can be done by computers using the brute-force method. However, it will require a significant amount of computational power to brute-force search space. To speed up the process simulations can be distributed between hundreds and thousands of nodes. To further optimize this problem we can use macro optimization methods such as Particle Swarm Optimization (PSO), and let the nodes communicate with each other like a swarm of birds. Using this methodology one can find an acceptable solution within a few days without any supervision. This work also proves that in some cases this approach could improve the best-known solution.

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Lightweight HI source finding for next generation radio surveys

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ASTRONOMY AND COMPUTING 2022年 41卷

作者： Tolley, E. Korber, D. Galan, A. Peel, A. Sargent, M. T. Kneib, J. -P. Courbin, F. Starck, J. -L. Ecole Polytech Fed Lausanne EPFL Lab Astrophys Observ Sauverny CH-1290 Versoix Switzerland Ecole Polytech Fed Lausanne EPFL SCITAS CH-1015 Lausanne Switzerland Int Space Sci Inst Hallerstr 6 CH-3012 Bern Switzerland Univ Sussex Astron Ctr Dept Phys & Astron Brighton BN19QH E Sussex England CEA Saclay Dept Astrophys F-91191 Gif Sur Yvette France Ecole Polytech Fed Lausanne EPFL Lab Astrophys Observ Sauverny CH-1290 Versoix Switzerland

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modules for the denoising, detection and characterization of HI sources in 3D spectral data. LiSA was developed and tested on the Square Kilometer Array Science Data Challenge 2 dataset, and contains modules and pipelines for easy domain decomposition and parallel execution. LiSA contains algorithms for 2D-1D wavelet denoising using the starlet transform and flexible source finding using null-hypothesis testing. These algorithms are lightweight and portable, needing only a few user-defined parameters reflecting the resolution of the data. LiSA also includes two convolutional neural networks developed to analyze data cubes which separate HI sources from artifacts and predict the HI source properties. All of these components are designed to be as modular as possible, allowing users to mix and match different components to create their ideal pipeline. We demonstrate the performance of the different components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources with SNR > 3 and accurately predict their properties. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： methods Data analysis Techniques image processing

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EDAML 2022 Invited Speaker 9: Thermal and Power Monitoring and Estimation for Commercial Multicore Processors—A Machine Learning Perspective

EDAML 2022 Invited Speaker 9: Thermal and Power Monitoring a...

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Sheldon Tan University of California at Riverside

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

In this talk, I will present our recent work from my VSCLAB at UC Riverside on machine learning based thermal map and power density map estimation methods for commercial multi-core CPUs. I will first present the task-dependent hot spot or heat sources identification for multi-core processors based on measured thermal maps. In our work, instead of using traditional functional unit powers as input, the new models are directly based on the on-chip real-time high level chip utilizations and thermal sensor information of commercial chips without any assumption of additional physical sensors requirement. We first framed the problem as the static or transient mapping between the chip utilizations and thermal maps. To build the transient thermal model, we utilized temporal-aware long-short-term-memory (LSTM) neural networks with system-level variables such as chip frequency, voltage, and instruction counts as inputs. Instead of a pixel-wise heatmap estimation, we used 2D spatial discrete cosine transformation (DCT) on the heatmaps so that they can be expressed with just a few dominant DCT coefficients. Second, we explored generative learning for the full-chip thermal map estimation problem. In our work, we treated the thermal modeling problem as an image-generation problem using the generative neural networks. The resulting thermal map estimation method, called ThermGAN can provide tool-accurate full-chip transient thermal maps from the given performance monitor traces of commercial off-the-shelf multi-core processors. Third, I will present a new full-chip power map estimation method for commercial multi-core processors. We proposed to use a simple first-principle based 2D spatial Laplace method to generate the power maps from the measured or simulated thermal maps. Then I will show how we can obtain the thermal map estimation for multi-processor chips under practical heat sink cooling conditions in which no thermal maps can be measured.

关键词： Estimation Multicore processing Transient analysis Thermal management Semiconductor device measurement Integrated circuit modeling Heating systems

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Perturbation of image-based Malware Detection with Smali level morphing techniques

Perturbation of Image-based Malware Detection with Smali lev...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Federico Gerardi Giacomo Iadarola Fabio Martinelli Antonella Santone Francesco Mercaldo Geckosoft Pisa Italy Institute of Informatics and Telematics National Research Council of Italy (CNR) Pisa Italy University of Molise Campobasso Italy

Considering the weaknesses of signature-based approaches adopted by current antimalware, from both academic and industrial side there is a boost in the development of techniques exploiting artificial intelligence, where one of the most promising are based on the representation of application under analysis as image. In order to understand whether these approaches can be effectively adopted in the real-world, starting from a detector based on deep learning, in this paper we evaluate the resilience of these approaches when morphed samples are considered. We present DexWave, a tool aimed to automatically inject perturbations techniques targeting the smali code representation of Android applications. The experimental analysis demonstrate that image-based malware classifier are vulnerable to simple perturbations attack.

关键词： Deep learning Codes Perturbation methods Detectors Malware Artificial intelligence Resilience

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Domain Adaptive Semantic Segmentation via image Translation and Representation Alignment

Domain Adaptive Semantic Segmentation via Image Translation ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingxuan Kang Bin Zang Weipeng Cao School of Computer Science University of Liverpool Liverpool UK School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixel-centroid triplet loss is designed to explicitly minimize the intra-class feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 → Cityscapes and SYNTHIA → Cityscapes scenarios.

关键词： image segmentation Adaptation models Semantics Pipelines Predictive models Linear programming Robustness

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RETRACTED: image Recognition and Simulation Based on distributed Artificial Intelligence (Retracted Article)

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COMPLEXITY 2021年第1期2021卷

作者： Fan, Tao Shanghai Univ Engn Sci Dept Elect & Elect Engn Shanghai 201620 Peoples R China

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients (HOG) feature extraction and Support Vector Machine (SVM) classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis (PCA) to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.

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Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

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CIRCUITS SYSTEMS AND SIGNAL processing 2020年第2期39卷 543-570页

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Simultaneous localization and map construction (SLAM) tasks have been proven to benefit greatly from the depth information of the environment. In this paper, we first present an unsupervised end-to-end learning framework for the task of monocular depth and camera motion estimation from video sequences. The difference between our work and the existing unsupervised methods is that we not only use image reconstruction for supervising but also exploit the pose estimation method used in traditional SLAM approaches to enhance the supervised signal and add extra training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task superior to the supervised methods and outperforms the previous state-of-the-art unsupervised approach by 13.5% on KITTI dataset. For the pose estimation task, our method performs comparably to the supervised methods that use ground-truth pose data for training. Besides, our unsupervised learning framework can significantly accelerate the initialization process of the traditional ORB-SLAM system and effectively improve the accuracy of environmental mapping in strong lighting and weak texture scenes.

关键词： Monocular depth estimation Pose estimation Unsupervised learning Visual SLAM system

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Comparative analysis of image projection-based descriptors in Siamese neural networks

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ADVANCES IN ENGINEERING SOFTWARE 2021年 154卷 102963-102963页

作者： Kertesz, Gabor Szenasi, Sandor Vamossy, Zoltan Obuda Univ John von Neumann Fac Informat Budapest Hungary J Selye Univ Fac Econ Komarno Slovakia

Low-level object matching can be done using projection signatures. In case of a large number of projections, the matching algorithm has to deal with less significant slices. A trivial approach would be to do statistical analysis or apply machine learning to determine the significant features. To take adjacent values of the projection matrices into account, a convolutional neural network should be used. To compare two matrices, a Siamese structure of convolutional heads can be applied. In this paper, an experiment is designed and implemented to analyze the object matching performance of Siamese Convolutional Neural Networks based on multi-directional image projection data. A backtracking search-based Neural Architecture Generation method is used to create convolutional architectures, and a Master/Worker structured distributed processing with highly efficient scheduling based on the Longest processing Times-heuristics is used for parallel training and evaluation of the models. Results show that the projection-based methods are Pareto optimal in terms of one-shot classification accuracy and memory consumption.

关键词： Siamese neural networks Multi-directional image projections Neural architecture generation one-shot classification accuracy Vehicle re-identification

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image Distillation Based Screening for X-ray Crystallography Diffraction images

Image Distillation Based Screening for X-ray Crystallography...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Lingyao Chen Kang Xu Xiaoying Zheng Yongxin Zhu Yi Jing Shanghai Information Technology Research Center Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

The modern synchrotron radiation facilities are producing massive diffraction images, which present a severe problem for data processing due to the high dimensionality of imaging data. Feature recognition and selection based deep learning methods have been developed to analyze data automatically. One crucial step is to use AI to screen out the diffraction images without Bragg spots. This paper proposes a feature distillation based approach for screening. It helps to reduce over 40% raw data volume and greatly alleviates the post processing workload faced by scientists.

关键词： Heating systems Deep learning Diffraction Imaging X-ray diffraction Data processing Crystallography

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The research of implementation method of canny edge detection of video on FPGA

The research of implementation method of canny edge detectio...

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2020 International Conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Tan, Li Quan He, Bin School of Information Science and Technology Beijing University of Chemical Technology 100029 China

ISBN: (纸本)9781665404488

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time;then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection. © 2020 IEEE.

关键词： image enhancement

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