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检索条件"任意字段=2024 International Symposium on Computer Architecture and High Performance Computing Workshops"

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RAHP: A Redundancy-aware Accelerator for high-performance Hypergraph Neural Network 57

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

引用

57th Annual IEEE/ACM international symposium on Microarchitecture, MICRO 2024

作者： Yu, Hui Zhang, Yu He, Ligang Zhao, Yingqi Li, Xintao Xin, Ruida Zhao, Jin Liao, Xiaofei Liu, Haikun He, Bingsheng Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China University of Warwick Department of Computer Science United Kingdom National University of Singapore Singapore

ISBN: (纸本)9798350350579

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： accelerator HyperGNN redundancy-aware

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QNTN: Establishing a Regional Quantum Network in Tennessee

QNTN: Establishing a Regional Quantum Network in Tennessee

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high performance computing, Networking, Storage and Analysis, SC-W: workshops of the international Conference for

作者： Mohamed Shaban Muhammad Ismail Mariam Kiran Cybersecurity Education Research and Outreach Center (CEROC) and Computer Science Department Tennessee Tech University TN USA Department of Mathematics Faculty of Education Alexandria University Egypt Oak Ridge National Laboratory TN USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This paper investigates the design of a regional Quantum Network in Tennessee (QNTN) that will connect three quantum local area networks in different cities. We explore two approaches for achieving this interconnection: deploying a satellite constellation in the space layer and employing high-altitude platforms (HAPs) in the aerial layer. Our comparison reveals that a space-ground architecture that uses 108 satellites provides 55.17% coverage of the day and handles 57.75% of entanglement distribution requests with an average fidelity of 0.96. In contrast, the air-ground architecture delivers full-day coverage, fulfills 100% of requests, and achieves a higher average fidelity of 0.98. However, HAPs face significant challenges such as limited operational time, sensitivity to vibrations and weather conditions, and the need for continuous maintenance. This paper contributes to the understanding of optimal architecture for regional quantum networks, highlighting the trade-offs between satellite-based and air-ground approaches.

关键词： Vibrations Satellite constellations Satellites Atmospheric modeling Urban areas computer architecture Environmental factors Air to ground communication Quantum networks Meteorology

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Ph.D. Project: Modernizing high-Level Hardware Design Workflows

Ph.D. Project: Modernizing High-Level Hardware Design Workfl...

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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Rishov Sarkar Cong Callie Hao School of Electrical and Computer Engineering Georgia Institute of Technology

The field of machine learning (ML) is growing at a break-neck pace. As ML models increasingly demand real-time performance, energy efficiency, and high throughput, researchers look to customized computing architecture... 详细信息

ISBN: (数字)9798350372434

ISBN: (纸本)9798350372441

关键词： Computational modeling Machine learning computer architecture Throughput Real-time systems Hardware Energy efficiency

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Hybrid Solver Techniques for Wideband Antennas 27

Hybrid Solver Techniques for Wideband Antennas

引用

27th international symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Kandregula, Venkat Reddy Lazaridis, Pavlos I. Ahmed, Qasim Zeeshan Khan, Faheem A. Zaharis, Zaharias D. Poulkov, Vladimir University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki Greece Technical University of Sofia Faculty of Telecommunications Sofia Bulgaria

ISBN: (纸本)9798350392319

Evaluating the performance of an antenna using commercially available electromagnetic (EM) solvers such as computer simulation technology (CST) studio suite has become crucial. In this context, a unidirectional advanced technique is used for simulating a Printed Log-Periodic Antenna (PLPDA) enclosed in a fixture with foam. This method employs a hybrid solver approach, in which the PLPDA with foam (source) is simulated using a transient solver, and the fixture (platform) is simulated using an integral equation solver. The proposed technique significantly reduces the simulation time and RAM usage while maintaining high accuracy. It is a viable alternative to traditional full-wave analysis, where the source and platform are simulated using a single solver or bidirectional simulation techniques. A comparative analysis with measured radiation patterns demonstrates the effectiveness of this method, providing a reliable and efficient solution for antenna design and performance evaluation. © 2024 IEEE.

关键词： Log periodic antennas

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Time Distance Aware for Multi-component Graph Collaborative Filtering

Time Distance Aware for Multi-component Graph Collaborative...

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PhD symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

作者： Batsuuri, Tseesuren Xue, Shan Yang, Jian Wu, Jia School of Computing Macquarie University SydneyNSW Australia

ISBN: (纸本)9789819614820

Graph Convolutional Networks (GCNs) have gained prominence in collaborative filtering (CF) recommendation systems for capturing intricate signals using high-order structural data. However, GCN-based models focus solely on these signals, neglecting the sparse nature of data and overlooking important aspects like temporal signals in user preferences and baseline signals in users or items, leading to sub-optimal performance. To address these issues, this paper introduces a novel multi-component CF model that integrates GCNs with baseline and temporal components. The integrated model learns user and item representations from multiple perspectives, enhancing performance and robustness across various datasets. Experiments conducted on the MovieLens and Douban datasets demonstrate the superiority of this approach over state-of-the-art models, reducing RMSE by up to 4.7%, while improving NCDG by up to 5.1% compared to pure GCN-based CF (https://***/tseesurenb/wise2024_***). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

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IEEE international Conference on Cluster computing workshops and Posters (CLUSTER workshops)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences high performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Structural Identification and Quality Inspection of Underground Caverns in Hydroelectric Engineering via computer Vision and Neural Network computing 24

Structural Identification and Quality Inspection of Undergro...

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2024 international symposium on Integrated Circuit Design and Integrated Systems, ICDIS 2024

作者： Li, Xiaohu Li, Meng Yang, Chaoyue Zha, Li Luo, Shaohua Zou, Detao Ding, Wanqin China Three Gorges Construction Engineering Corporation Beijing China Xi'an China Energy Development Co. Ltd Zhangye China

ISBN: (纸本)9798400718229

Ensuring the structural integrity and operational safety of underground caverns in hydropower projects is critical for the efficient and safe functioning of these facilities. Traditional inspection methods are time-consuming, prone to human error, and often inadequate for capturing complex structural details under challenging conditions. With the rapid advancement of artificial intelligence, neural networks have emerged as powerful tools for automating and improving the accuracy of structural inspections. In this study, we propose a novel approach leveraging the ConvNeXt-B model combined with a Weighted Channel Selection Module (WCSM) and Semantic Feature Enhancement to perform fine-grained image recognition of underground cavern structures. The WCSM enables the model to focus on high-dimensional and low-dimensional image features, filtering out redundant information and enhancing classification accuracy. Meanwhile, the Semantic Feature Enhancement Module improves the model's ability to capture detailed, discriminative features by grouping feature maps into semantic clusters. Experimental results on a specialized dataset of cavern images demonstrate that this approach significantly enhances recognition performance, addressing the limitations of manual inspections and advancing the use of neural networks in civil and industrial engineering. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Caves

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Building a portable parallel asynchronous PDE solver using Kokkos

Building a portable parallel asynchronous PDE solver using K...

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IEEE international Conference on high performance computing workshops (HiPCW)

作者： Ranjan Bhat Konduri Aditya Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem.

关键词： Scalability Partial differential equations high performance computing Conferences Buildings computer architecture Benchmark testing Libraries performance analysis Finite difference methods

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Beyond Training: A Zero-Shot Framework to Neural architecture and Accelerator Co-Exploration

Beyond Training: A Zero-Shot Framework to Neural Architectur...

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IEEE international Conference on Cluster computing workshops and Posters (CLUSTER workshops)

作者： Wei Fu Wenqi Lou Lei Gong Chao Wang Xuehai Zhou University of Science and Technology of China Hefei China

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

Recently, the co-exploration of neural architectures and accelerators has become imperative to achieve high accuracy and hardware performance. However, existing methods face a huge training burden during neural network search, which prolongs the search time and increases the task-switching deployment cost. Additionally, their limited hardware search space restricts the potential for optimal results. To address these issues, we propose an efficient zero-shot framework for quickly identifying optimized neural architectures and accelerators based on given requirements. First, we design a training-free proxy to rank the accuracy of the network efficiently, thereby eliminating the need for the network training process. Second, we conduct a finer-grained hardware search space to achieve optimal performance. Preliminary experiments demonstrate that, compared to prior methods, our approach enhances search efficiency by $\mathbf{9.67}\times$ while optimizing the hardware Energy-Delay Product (EDP) by up to $\mathbf{24.6}\times$ , without compromising network accuracy.

关键词： Training Accuracy Conferences Neural networks computer architecture Network architecture Transformers Hardware Software Space exploration

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DigiQ: A Scalable Digital Controller for Quantum computers Using SFQ Logic 28

DigiQ: A Scalable Digital Controller for Quantum Computers U...

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28th Annual IEEE international symposium on high-performance computer architecture (HPCA)

作者： Jokar, Mohammad Reza Rines, Richard Pasandi, Ghasem Cong, Haolin Holmes, Adam Shi, Yunong Pedram, Massoud Chong, Frederic T. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA Super Tech Chicago IL USA NVIDIA Santa Clara CA USA Amazon Braket Seattle WA USA HRL Labs LLC Malibu CA USA

ISBN: (纸本)9781665420273

The control of cryogenic qubits in today's superconducting quantum computer prototypes presents significant scalability challenges due to the massive costs of generating/routing the analog control signals that need to be sent from a classical controller at room temperature to the quantum chip inside the dilution refrigerator. Thus, researchers in industry and academia have focused on designing in-fridge classical controllers in order to mitigate these challenges. Due to the maturity of CMOS logic, many industrial efforts (Microsoft, Intel) have focused on Cryo-CMOS as a near-term solution to design in-fridge classical controllers. Meanwhile, Superconducting Single Flux Quantum (SFQ) is an alternative, less mature classical logic family proposed for large-scale in-fridge controllers. SFQ logic has the potential to maximize scalability thanks to its ultra-high speed and very low power consumption. However, architecture design for SFQ logic poses challenges due to its unconventional pulse-driven nature and lack of dense memory and logic. Thus, research at the architecture level is essential to guide architects to design SFQ-based classical controllers for large-scale quantum machines. In this paper, we present DigiQ, the first system-level design of a Noisy Intermediate Scale Quantum (NISQ)-friendly SFQ-based classical controller. We perform a design space exploration of SFQ-based controllers and co-design the quantum gate decompositions and SFQ-based implementation of those decompositions to find an optimal SFQ-friendly design point that trades area and power for latency and control while ensuring good quantum algorithmic performance. Our co-design results in a single instruction, multiple data (SIMD) controller architecture, which has high scalability, but imposes new challenges on the calibration of control pulses. We present software-level solutions to address these challenges, which if unaddressed would degrade quantum circuit fidelity given the imperfections of q

关键词： SFQ-based quantum gate Quantum optimal control Scalable quantum computer Cryogenic electronic

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