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Accelerating MRI Reconstruction on TPUs

Accelerating MRI Reconstruction on TPUs

引用

IEEE High Performance Extreme Computing conference (HPEC)

作者： Lu, Tianjian Marin, Thibault Zhuo, Yue Chen, Yi-Fan Ma, Chao Google Res Mountain View CA 94043 USA Harvard Med Sch Gordon Ctr Med Imaging Massachusetts Gen Hosp Boston MA 02115 USA

ISBN: (纸本)9781728192192

The advanced magnetic resonance (MR) image reconstructions such as the compressed sensing and subspace-based imaging are considered as large-scale, iterative, optimization problems. Given the large number of reconstructions required by the practical clinical usage, the computation time of these advanced reconstruction methods is often unacceptable. In this work, we propose using Google's Tensor processing Units (TPUs) to accelerate the MR image reconstruction. TPU is an application-specific integrated circuit (ASIC) for machine learning applications, which has recently been used to solve large-scale scientific computing problems. As proof-of-concept, we implement the alternating direction method of multipliers (ADMM) in TensorFlow to reconstruct images on TPUs. The reconstruction is based on multi-channel, sparsely sampled, and radial-trajectory k-space data with sparsity constraints. The forward and inverse non-uniform Fourier transform operations are formulated in terms of matrix multiplications as in the discrete Fourier transform. The sparsifying transform and its adjoint operations are formulated as convolutions. The data decomposition is applied to the measured k-space data such that the aforementioned tensor operations are localized within individual TPU cores. The data decomposition and the inter-core communication strategy are designed in accordance with the TPU interconnect network topology in order to minimize the communication time. The accuracy and the high parallel efficiency of the proposed TPU-based image reconstruction method are demonstrated through numerical examples.

关键词： Compressed sensing non-Cartesian MR image reconstruction parallel computing parallel imaging TensorFlow Tensor processing Unit

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VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution

VRFormer: 360-Degree Video Streaming with FoV Combined Predi...

引用

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

作者： Zhihao Zhang Haipeng Du Shouqin Huang Weizhan Zhang Qinghua Zheng Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University School of Continuing Education Xi'an Jiaotong University

360-degree video has shown great potential to the mainstream since its immersive experience. However, 360-degree video streaming requires ultrahigh bandwidth and low latency, which limit the improvement of user quality of experience (QoE). Currently, methods combining field of view (FoV) prediction and adaptive video streaming provide an effective method for addressing the above issues. However, existing FoV prediction methods based on recurrent neural networks (RNN) cannot capture long-range dependency from input to output. Current deep reinforcement learning (DRL)-based adaptive strategies fail to estimate the future bandwidth with high accuracy and fully explore the capability of VR devices. To ameliorate these limitations, we design a DRL-based 360-degree video streaming method named VRFormer with FoV combined prediction and super resolution (SR). First, we adopt a content-aware transformer-based encoder-decoder network to make the long-term FoV prediction. It combines the user's head movement history, eye-tracking history, and user attention extracted from a convolutional neural network (CNN)-based network. Second, we introduce a DNN-based SR network running on VR devices to reconstruct high-definition video content. Finally, we apply a DRL-based network to adaptively allocate rates for future tiles and dynamically control video content reconstruction. Experiments have verified that the proposed method can effectively improve the quality of experience (QoE) of the user's viewing experience compared to the state-of-the-art methods.

关键词： Recurrent neural networks Superresolution Bandwidth Reinforcement learning Prediction methods Streaming media Transformers

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A Graph Theory-Based Clustering Method for Power System Networks

A Graph Theory-Based Clustering Method for Power System Netw...

引用

Clemson-University Power Systems conference (PSC)

作者： Jayawardene, Iroshani Herath, Pramod Venayagamoorthy, Ganesh Kumar Clemson Univ Holcombe Dept Elect & Comp Engn Real Time Power & Intelligent Syst Lab Clemson SC 29634 USA Univ KwaZulu Natal Sch Engn Durban South Africa

ISBN: (纸本)9781728193847

With the increasing human population and requirements, the current electric power grid is becoming complex and expanding rapidly. Increasing integration of renewable energy sources and other advanced metering technologies introduces a complex, expanded and distributed power system infrastructure. Real-time operation and control of large-scale power system networks have become a challenging task due to these reasons. Power system clustering is a feasible solution to overcome these challenges. Clustering utilizes parallel processing capabilities of control methods and provides adequate methods for real-time power system control. In this paper, a graph theory-based clustering approach is presented for large interconnected power system networks. The algorithm is tested on multiple power system networks from small to large scale and the performance is analyzed. Typical results indicate that the method can provide clusters with uniform sizes within a short time limit for any type of power system network. The clusters observed are applied in solving optimal power flow (OPF) problem.

关键词： clustering graph theory parallel processing power networks real-time operation

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Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with parallel Association Rule Mining and Scalable Recommenders 44

Design and Evaluation of an HPC-based Expert System to speed...

引用

44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Barakat, C. Riedel, M. Brynjolfsson, S. Cavallaro, G. Busch, J. Sedona, R. School of Engineering and Natural Sciences University of Iceland Iceland Jülich Supercomputing Centre Forschungszentrum Jülich Germany

ISBN: (纸本)9789532331011

Given the Covid-19 pandemic, the retail industry shifts many business models to enable more online purchases that produce large transaction data quantities (i.e., big data). Data science methods infer seasonal trends about products from this data and spikes in purchases, the effectiveness of advertising campaigns, or brand loyalty but require extensive processing power leveraging High-Performance Computing to deal with large transaction datasets. This paper proposes an High-Performance Computing-based expert system architectural design tailored for 'big data analysis' in the retail industry, providing data science methods and tools to speed up the data analysis with conceptual interoperability to commercial cloud-based services. Our expert system leverages an innovative Modular Supercomputer Architecture to enable the fast analysis by using parallel and distributed algorithms such as association rule mining (i.e., FP-Growth) and recommender methods (i.e., collaborative filtering). It enables the seamless use of accelerators of supercomputers or cloud-based systems to perform automated product tagging (i.e., residual deep learning networks for product image analysis) to obtain colour, shapes automatically, and other product features. We validate our expert system and its enhanced knowledge representation with commercial datasets obtained from our ON4OFF research project in a retail case study in the beauty sector. © 2021 Croatian Society MIPRO.

关键词： Sales

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High-Performance Truss Analytics in Arkouda

High-Performance Truss Analytics in Arkouda

引用

International conference on High Performance Computing

作者： Zhihui Du Joseph Patchett Oliver Alvarado Rodriguez Fuhuan Li David A. Bader Department of Data Science New Jersey Institute of Technology Newark USA

In graph analytics, a truss is a cohesive subgraph based on the number of triangles supporting each edge. It is widely used for community detection applications such as social networks and security analysis, and the performance of truss analytics highly depends on its triangle counting method. This paper proposes a novel triangle counting kernel named Minimum Search (MS). Minimum Search can select two smaller adjacency lists out of three and uses fine-grained parallelism to improve the performance of triangle counting. Then, two basic algorithms, MS-based triangle counting, and MS-based support updating are developed. Based on the novel triangle counting kernel and the two basic algorithms above, three fundamental parallel truss analytics algorithms are designed and implemented to enable different kinds of graph truss analysis. These truss algorithms include an optimized K-Truss algorithm, a Max-Truss algorithm, and a Truss Decomposition algorithm. Moreover, all proposed algorithms have been implemented in the parallel language Chapel and integrated into an open-source framework, Arkouda. Through Arkouda, data scientists can efficiently con-duct graph analysis through an easy-to-use Python interface and handle large-scale graph data in powerful back-end computing resources. Experimental results show that the proposed methods can significantly improve the performance of truss analysis on real-world graphs compared with the existing and widely adopted list intersection-based method. The implemented code is publicly available from GitHub (https://***/Bears-R-Us/arkouda-njit).

关键词： parallel languages Codes Social networking (online) Search methods image edge detection High performance computing parallel processing

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Searching for Fast Model Families on Datacenter Accelerators

Searching for Fast Model Families on Datacenter Accelerators

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Sheng Tan, Mingxing Pang, Ruoming Li, Andrew Cheng, Liqun Le, Quoc, V Jouppi, Norman P. Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than Efficient-Net (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100.

关键词： Convolutional codes Computer vision Convolution Computational modeling Search methods parallel processing Hardware

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Fast parallel bounding volume hierarchy construction

Fast parallel bounding volume hierarchy construction

引用

2020 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2020

作者： Guo, Xiaozi Zhang, Juan Zhou, Mingquan Shaanxi Normal University School of Computer Science Xi'an China Shaanxi Normal University School of Journalism and Communication Xi'an China

ISBN: (纸本)9781728160665

Our algorithm optimized the parallel structure and speeded up the parallel construction based on the locally-ordered clustering to build the bounding volume hierarchy (BVH). We use the bottom-up approach to build the BVH, merging nearest clusters and subnearest clusters in each iteration. Our algorithm was verified in CUDA and evaluated in GPU ray tracing scenarios. Compared with other methods, our method is more efficient in building rate and effectively generates a more balanced BVH under complex scenarios. © 2020 IEEE.

关键词： Ray tracing

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distributed image Retrieval Base on LSH Indexing on Spark 1st

Distributed Image Retrieval Base on LSH Indexing on Spark

引用

1st International conference on Big Data and Security, ICBDS 2019

作者： Hou, Zelei Huang, Chao Wu, Jiagao Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of Visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Indexing (of information)

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On the Choice of Coil Combination Weights for Phase-Sensitive GRAPPA Reconstruction in Multichannel SWI 3rd

On the Choice of Coil Combination Weights for Phase-Sensitiv...

引用

3rd International conference on Computer Vision and image processing (CVIP)

作者： Madhusoodhanan, Sreekanth Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Med Image Comp & Signal Proc Grp Trivandrum Kerala India

ISBN: (纸本)9789813290884;9789813290877

The feasibility of applying Generalized Auto-calibrating Partially parallel Acquisition (GRAPPA) techniques has been established, with a two-fold or more reduction in scan time without compromising vascular contrast in Susceptibility Weighted Imaging (SWI) by choosing an optimal sensitivity map for combining the coil images. The overall SNR performance in GRAPPA is also dependent on the weights used for combining the GRAPPA reconstructed coil images. In this article, different methods for estimating the optimal coil combination weights are qualitatively and quantitatively analysed for maximizing the structural information in the tissue phase. The performance of various methods is visually evaluated using minimum Intensity Projection (mIP), Among the three methods, sensitivity estimated using the dominant eigenvector mentioned as ESPIRiT-based sensitivity in this article shows superior performance over the other two methods including estimating the sensitivity from the centre k-space line and from reconstructed channel images. Combining channel images using ESPIRiT sensitivity shows its ability to preserve the local phase variation and reduction in noise amplification.

关键词： Coil combination parallel imaging Sensitivity SWI

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Underwater LED light MIMO telecentric optical path technology research

Underwater LED light MIMO telecentric optical path technolog...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiangnan Liu Guojin Peng Jiapeng Su Provincial Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin China

The system capacity can be further improved by building Light Emitting Diode (LED) light Multiple-Input Multiple-Output (MIMO) on the basis of hemispherical array structure, but the MIMO capacity is limited by the degree of sub-channel interference. To reduce the sub-channel interference of hemispherical arrays, this paper proposes a method to control the sub-channel interference of LED optical MIMO based on the telecentric optical path, which can achieve the sub-channel isolation of LED optical MIMO in hemispherical array structure by using the restriction effect of the optical diaphragm on stray light and maintain the stable sub-channel isolation in a large communication range. Compared with the conventional Singular Value Decomposition (SVD)/Space-time Block Coding (STBC)/ optical zoom and other electrical and optical domain LED optical MIMO sub-channel interference control methods, the proposed method has better Bit Error Ratio (BER) performance and lower structural complexity, and improves the robustness of sub-channel correlation to communication distance variation.

关键词： Stimulated emission Interference Stray light Light emitting diodes Ubiquitous computing Adaptive optics Encoding

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