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Analysis of performance and Optimization in MindSpore on Ascend NPUs 29

Analysis of Performance and Optimization in MindSpore on Asc...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Bangchuan Yang, Chuying Zhu, Rui Liu, Xiao Zhou, Mingyao Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies Hangzhou China Beijing Huawei Digital Technology Co. Ltd. Beijing China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350330717

With the rapid advancement of artificial intelligence, the complexity and depth of deep neural networks continues to grow, placing higher demands on computational power. To meet these requirements, various manufacturers have developed specialized computing processors for the training process of deep learning, such as Huawei's Ascend Neural Processing Unit (NPU). In order to fully leverage the capabilities of the Ascend NPU, Huawei has introduced the MindSpore deep learning framework. The computational performance of deep learning frameworks plays a critical role for developers. However, there is a lack of comprehensive research on the analysis of performance and optimization in MindSpore framework on the Ascend NPU, leading to a scarcity of relevant references for deep learning development utilizing MindSpore on the Ascend NPU. To address this gap, this study examined the performance and optimization of MindSpore on Ascend NPUs through detailed experiments involving diverse workloads and multiple analysis metrics. The analysis is conducted at three levels: operations, models, and techniques, investigating the operations configurations, performance bottleneck, along with techniques selections, within the training process of DNNs. Consequently, this study offers significant insights and guidance for DL researchers and practitioners using MindSpore on Ascend NPUs. © 2023 IEEE.

关键词： Ascend NPU Deep Learning Deep Learning Framework high performance Computing MindSpore

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Energy Consumption Optimization of Swin Transformer Based on Local Aggregation and Group-Wise Transformation 4

Energy Consumption Optimization of Swin Transformer Based on...

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4th International conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Liu, Yuhong Chen, Shengbo Lei, Zhou Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350326444

The Transformer is a natural language processing model that uses attention mechanisms, originally introduced by Google in 2017. Due to its excellent feature extraction capabilities, researchers in the industry have explored the application of Transformer in computer vision tasks. Following the success of the Vision Transformer in 2020, the Swin Transformer was introduced, which outperforms traditional convolutional neural networks on various benchmarks in the vision field. As a result, Swin Transformer has become a popular backbone for practical production applications. While Swin Transformer excels in accuracy, its large parameter size and high energy consumption pose challenges for deployment at the edge. Addressing this issue is crucial for the wide-scale adoption of Swin Transformer. Current research on energy consumption for Transformer-based models is still in its early stages. To reduce model size and energy consumption while preserving accuracy, this study focuses on model compression. Specifically, we propose a combination of group-wise transformation and local aggregation methods from traditional CNN models. By introducing local aggregation and group-wise transformation calculation methods while maintaining the original shifted window attention mechanism of Swin Transformer, the proposed approach effectively reduces the number of model parameters. Our experimental results show that the Swin-Transformer model trained with this method on an RTX-3090 graphics card can save up to 24% of energy, with an accuracy improvement of about 1%. © 2023 IEEE.

关键词： Energy utilization

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Hybrid Beamforming-based Beam Tracking using UKF in Massive MIMO Systems

Hybrid Beamforming-based Beam Tracking using UKF in Massive ...

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2023 International conference on Electronics, Information, and Communication, ICEIC 2023

作者： Sim, Yuna Sin, Seungseok Cho, Jihun Kim, Kyunam Moon, Sangmi Hwang, Intae Chonnam National University Dept. of Ict Convergences System Engineering Gwangju Korea Republic of Chonnam National University Dept. of Electronics and Computer Engineering Gwangju Korea Republic of Chonnam National University Deptartment of Ict Convergences System Engineering Gwangju Korea Republic of Alps Electric Korea Co. Ltd. Gwangju Korea Republic of Korea Nazarene University Department of It Artificial Intelligence Cheonan Korea Republic of Chonnam National University Dept. of Electronic Engineering Dept. of Ict Convergence System Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350320213

Millimeter wave frequencies are one of the promising technologies to support high data rates in 5G wireless communication systems, but short wavelengths due to high frequencies cause signal attenuation and path loss. To compensate for this problem, in this paper, we apply the hybrid beamforming technique and try to track the exact beam angle using the Unscented Kalman filter to obtain the complete beamforming gain. As a result of the simulation, it can be seen that the proposed algorithm exhibits good Mean Square Error (MSE) performance with a moderate level of implementation complexity. However, the computational complexity that arises when hybrid beamforming is applied still remains a problem to be solved. Therefore, in the course of future research, if a deep neural network is applied in terms of beamformer and combiner, it will be able to show a higher level of performance with a lower level of complexity. © 2023 IEEE.

关键词： beam tracking hybrid beamforming unscented Kalman filter

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Identifying brain disease genes via integrating brain imaging and molecular network

Identifying brain disease genes via integrating brain imagin...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Wei Wang, Yuxian Peng, Jiajie Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350386226

Identifying genes associate to brain diseases is crucial for uncovering the related biological mechanisms and for advancing therapeutic interventions. Due to the ongoing advancements in network-based computational approaches, biological networks, especially molecular networks, provide valuable insights for predicting disease genes. However, many methods have ignored brain imaging data when exploring brain disease genes, despite its widely use in neuroscience research. In this paper, we propose a novel framework, Deep Interactive AutoEncoder (DIAE), which integrates brain imaging and molecular-based gene networks to predict brain disease genes. DIAE first constructs a gene association network based on brain imaging and high-resolution whole brain-whole gene expression data. Subsequently, a deep and interactive multi-network integration method is introduced to learn low-dimensional features of genes by combining the brain imaging-based network with other molecular-based gene networks. Finally, these features are utilized to predict brain disease genes using a support vector machine (SVM) model. For performance evaluation, we compare DIAE with three existing state-of-the-art methods in the context of disease gene identification across four brain diseases. The experimental results show the superior performance of DIAE and highlight the effectiveness of the brain imaging-based gene network for predicting brain disease genes. © 2024 IEEE.

关键词： Support vector machines

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30th International Joint conference on Industrial Engineering and Operations Management, IJCIEOM 2024

30th International Joint Conference on Industrial Engineerin...

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30th International Joint conference on Industrial Engineering and Operations Management, IJCIEOM 2024

ISBN: (纸本)9783031807848

The proceedings contain 37 papers. The special focus in this conference is on Industrial Engineering and Operations Management. The topics include: Proposal for an Optimized Model to Analyze Customer Satisfaction—B2B;mechatronic Framework for Smart Beehives: Prototyping and Applications Perspective;digital Fashion: The Impact of Service Quality on Customer Satisfaction;computer Vision as a Resource in Smart Warehouses: A Systematic Review;port Logistics Emissions Control Using Machine Learning to Coordinate the Truck Flow;Evolution of Logistics Service Providers: A Study of 5 PL;reinforcement Learning and Discrete Event Simulation Applied to Production Scheduling: A Scoping Review;a Simulation Model to Determine Production Shifts for Pallet Packaging;the Enhanced Factory for Extra-Terrestrial Space Technology Operations: Conceptualization and Scenarios’ Definition;digital Twins in Asset Prognosis and Health Management: Definitions, Applications, State of the Art, and Future Trends;Benefits of ERP Information Systems in ESG Management Within Organizations;an Assignment Model for high-Cognitive-Workload Maintenance Activities in Industry 5.0;Forecast of Brazilian GDP Growth Through Consumption of Diesel Oil and Electrical Energy;impact of Distribution Configurations on Supply Chain performance: A System Dynamics Approach;lean Healthcare Management Through Kaizen in a Public Brazilian Hospital;multicriteria Decision Analysis for Natural Risk Management Context: Knowledge Mapping and Analysis (2013–2023);assessment of a Project Management Maturity Diagnostic Tool;Utilizing GPT-4 in the Selection of Health-Tech Startups for a Brazilian Acceleration program: A Validation Study with 168 Startups;Applicability Analysis of the Power BI with the OKR Methodology in the Strategic Planning Enhancement;customer Satisfaction in Tourism Services Center.

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DDMI: DOMAIN-AGNOSTIC LATENT DIFFUSION MODELS FOR SYNTHESIZING high-QUALITY IMPLICIT NEURAL REPRESENTATIONS 12

DDMI: DOMAIN-AGNOSTIC LATENT DIFFUSION MODELS FOR SYNTHESIZI...

引用

12th International conference on Learning Representations, ICLR 2024

作者： Park, Dogyun Kim, Sihyeon Lee, Sojin Kim, Hyunwoo J. Department of Computer Science Korea University Seoul Korea Republic of

Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains. These models opened the door for domain-agnostic generative models, but they often fail to achieve high-quality generation. We observed that the existing methods generate the weights of neural networks to parameterize INRs and evaluate the network with fixed positional embeddings (PEs). Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation. To address this limitation, we propose Domain-agnostic Latent Diffusion Model for INRs (DDMI) that generates adaptive positional embeddings instead of neural networks' weights. Specifically, we develop a Discrete-to-continuous space Variational AutoEncoder (D2C-VAE) that seamlessly connects discrete data and continuous signal functions in the shared latent space. Additionally, we introduce a novel conditioning mechanism for evaluating INRs with the hierarchically decomposed PEs to further enhance expressive power. Extensive experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance Fields, and videos, with seven benchmark datasets, demonstrate the versatility of DDMI and its superior performance compared to the existing INR generative models. Code is available at https://***/mlvlab/DDMI. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Embeddings

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FPGA Design of RISC-V MCU Collaborative Industrial Printer Control System 6

FPGA Design of RISC-V MCU Collaborative Industrial Printer C...

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6th IEEE International conference on Knowledge Innovation and Invention, ICKII 2023

作者： Yen, Mao-Hsu Lin, Tzu-Feng Tsou, Cheng-Hao Yu, Yan-Wei Lin, Yih-Hsia Ku, Yuan-Fu Kao, Chien-Ting National Taiwan Ocean University Dept. of Computer Science and Engineering Keelung Taiwan Ming Chuan University Dept. of Electronic Engineering Taoyuan Taiwan Taiwan Testing and Certification Center Taoyuan Taiwan Dept. of Bureau of Standards Taipei Taiwan

ISBN: (纸本)9798350323535

The industrial printer control system was proposed in this research with a hardware architecture to support four print heads simultaneously. The system quickly and accurately calculated the printing distance and time of inkjet printing to achieve high-quality printing effects. In terms of vertical resolution, the system provided options such as 600, 300, 150, and 75 DPI. Horizontally, the system performed various DPI printing by detecting and calculating the speed of the rotary encoder. Following high-quality printing standards, the printing performance of industrial printers was enhanced, and the fastest printing speed reached 943 m/min. This industrial printer control system was implemented on an Altera FPGA incorporating the design of a reduced instruction set computer-V (RISC-V) microcontroller unit (MCU) [2] and an in-circuit debugger (ICD). The RISC-V MCU has a three-stage pipeline architecture to improve performance through parallel processing. The design was streamlined and modular to reduce costs, and an EBI BUS was added to communicate with peripheral devices. The ICD was used to monitor the status of the printing control system at any time and display the printing situation on the software platform. These comprehensive functions allowed industrial printers to have more complete functions and expandability. Using the SystemVerilog hardware description language, ModelSim's HDL simulation software was modified and tested. The functionality was verified through the field-programmable gate array (FPGA) program to ensure that it's correct. The implementation results can achieve the functions of an industrial printer control system with a speed of 100MHz. © 2023 IEEE.

关键词： computer hardware description languages

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JIFF: Jointly-aligned Implicit Face Function for high Quality Single View Clothed Human Reconstruction

JIFF: Jointly-aligned Implicit Face Function for High Qualit...

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

作者： Cao, Yukang Chen, Guanying Han, Kai Yang, Wenqi Wong, Kwan-Yee K. Univ Hong Kong Hong Kong Peoples R China CUHK Shenzhen Future Network Intelligence Inst FNii Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details in their reconstructions. This largely degrades user experience in applications like 3D telepresence. In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach. We employ a 3D morphable face model as our shape prior and compute space-aligned 3D features that capture detailed face geometry information. Such space-aligned 3D features are combined with pixel-aligned 2D features to jointly predict an implicit face function for high quality face reconstruction. We further extend our pipeline and introduce a coarse-to-fine architecture to predict high quality texture for our detailed face model. Extensive evaluations have been carried out on public datasets and our proposed JIFF has demonstrates superior performance (both quantitatively and qualitatively) over existing state-of-the-arts.

关键词： Solid modeling Three-dimensional displays Telepresence Shape Face recognition Computational modeling computer architecture

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An Improved Deep Learning-Based Approach to Urban Weather Radar Echo Extrapolation

An Improved Deep Learning-Based Approach to Urban Weather Ra...

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2023 IEEE International conference on Dependable, Autonomic and Secure Computing, 2023 International conference on Pervasive Intelligence and Computing, 2023 International conference on Cloud and Big Data Computing, 2023 International conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Sun, Jiawei Yang, Zhiyun Liu, Qi Zhang, Yonghong Liu, Xiaodong School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9798350304602

In recent years, the frequency of extreme precipitation events in urban areas has increased significantly, and the resulting disaster events have serious negative impacts on residents' daily lives. To take timely measures so as to reduce the losses caused by these events, meteorological engineers often perform extrapolation operation using maps sensed by urban weather radars, and then make now casting to estimate the intensity and extent of possible precipitation events. At present, deep learning techniques have been introduced to extrapolation tasks for their wide applicability to various conditions and their ability to mine potential patterns from large amounts of historical data. They obtained better results than traditional extrapolation methods. However, due to structural defects of these models themselves and the extremely uneven distribution of intensity values in radar echo maps, those high-intensity echoes that are closely related to potential severe convective weather events in maps predicted by existing deep models are often underestimated. To alleviate this problem, the MMST-LSTM, a structurally improved recurrent unit, is proposed in this paper, whose ability to capture and simulate the changing dynamics of high-intensity echoes is enhanced. The results of experiments that conducted on a radar echo dataset show the performance improvement of MMST-LSTM, and the alleviation of the intensity-underestimation problem in extrapolated maps compared with previous representative methods. © 2023 IEEE.

关键词： Extrapolation

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Analysis and performance Evaluation of OpenFlow Controller in SDN using N-Policy

Analysis and Performance Evaluation of OpenFlow Controller i...

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2023 International conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

作者： Al Ahmad, Mahmoud Diab, Maisa Patra, Sudhansu Shekhar University of Kalamoon College of Engineering It Department Damascus Suburb Syria Arab Private University for Science and Technology College of Informatics Engineering It Department Hama Syria Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350306927

The OpenFlow protocol facilitates the communication between the control and forwarding planes within a software-defined networking (SDN) framework. In SDN, the control plane is housed in a distinct entity known as the OpenFlow controller, which assumes the responsibility of making high-level determinations regarding the forwarding of network traffic. With the OpenFlow controller, network administrators can centrally program and manage network behavior, incorporating functionalities like traffic engineering, load balancing, QoS enforcement, security policies, and more. By segregating the control plane from the data plane, SDN utilizing OpenFlow empowers flexible network management, automation, and programmability. Through the utilization of the OpenFlow protocol, the OpenFlow controller interacts with the forwarding plane, typically implemented in network switches or routers. This approach offers a centralized perspective of the network and grants dynamic control over the behavior of network devices by installing flow entries into their forwarding tables. In this research, we use a single-controller N-policy finite buffer waiting queue model to investigate the system performance metrics. Numerical examples and different indices are presented to illustrate the proposed model. © 2023 IEEE.

关键词： Controllers

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