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DLPlib： A Library for Deep Learning Processor

Journal of computer Science & technology 2017年第2期32卷 286-296页

作者： Hui-Ying Lan Lin-Yang Wu Xiao Zhang Jin-Hua Tao Xun-Yu Chen Bing-Rui Wang Yu-Qing Wang Qi Guo Yun-Ji Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Microprocessor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Science University of Science and Technology of China Hefei 230026 China

Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework （e.g., TPU （tensor processing unit） is integrated into Tensorflow directly）. However, this will obstruct other frameworks to profit from the programming interface, The alternative approach is to design a framework-independent low-level library for deep learning processors （e.g., the deep learning library for GPU, cuDNN）. In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPIib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neur

关键词： deep learning processor API library DLPlib

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Broadband Waveguide Cloak for Water Waves

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Physical Review Letters 2019年第7期123卷 074501-074501页

作者： Siyuan Zou Yadong Xu Razafizana Zatianina Chunyang Li Xu Liang Lili Zhu Yongqiang Zhang Guohua Liu Qing Huo Liu Huanyang Chen Zhenyu Wang College of Civil Engineering and Architecture Zhejiang University Hangzhou 310058 China Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen University Xiamen 361005 China School of Physical Science and Technology and Jiangsu Key Laboratory of Thin Films Soochow University Suzhou 215006 China Ocean College Zhejiang University Zhoushan 316021 China Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA

Inspired by electromagnetic waveguide cloaks with gradient index metamaterials, we fabricated a broadband cloak with simply a gradient depth profile on the bottom and without any other structures on the top to confine water waves in a certain area for cloaking regions. The new physics of mode conversion for water waves is first found. The experimental and numerical simulation results are in good agreement and show that the presented device has a nice performance for various situations and is feasible over a broadband of working frequencies. Being easy to construct, this design is potentially of significance for port applications.

关键词： Cloaking Flow instability Hydrodynamic instabilities Thin fluid films Navier-Stokes equation

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Network Node Resource Risk Assessment Based on Bayesian Belief Network Model

Network Node Resource Risk Assessment Based on Bayesian Beli...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Jun Li YuQiang Liu Yan Niu Hui Zhang Department of Computer Science Hubei University of Technology Wuhan China School of Civil Engineering Architecture and Environment Hubei University of Technology Wuhan China

ISBN: (纸本)9781538676738;9781538676721

Network attacks will bring network node resource risks. In this paper, the time series of memory usage rate, network traffic and CPU utilization rate are selected as the research objects, and the network node resources are interrelated. Based on this feature, a network node resource risk assessment method based on Bayesian belief network is designed, and the single risk and total risk of network node resources are quantified. The results show that this method can effectively evaluate the network node resource risk, and fully consider the internal correlation between network node resources, and provide a new method for risk assessment of network node resources. The effect of this method is better than the traditional K-Means clustered method and decision tree method.

关键词： Risk management Bayes methods Civil engineering computer architecture Time series analysis Telecommunication traffic Correlation

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The Sequential Algorithm for Combined State of Charge and State of Health Estimation of Lithium Ion Battery based on Active Current Injection

arXiv

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arXiv 2019年

作者： Song, Ziyou Hou, Jun Li, Xuefeng Wu, Xiaogang Hu, Xiaosong Hofmann, Heath Sun, Jing Department of Naval Architecture and Marine Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States College of Electrical and Electronics Engineering Harbin University of Science and Technology Harbin150080 China Department of Automotive Engineering Chongqing University Chongqing400044 China

When State of Charge, State of Health, and parameters of the Lithium-ion battery are estimated simultaneously, the estimation accuracy is hard to be ensured due to uncertainties in the estimation process. To improve the estimation performance a sequential algorithm, which uses frequency scale separation and estimates parameters/states sequentially by injecting currents with different frequencies, is proposed in this paper. Specifically, by incorporating a high-pass filter, the parameters can be independently characterized by injecting high-frequency and medium-frequency currents, respectively. Using the estimated parameters, battery capacity and State of Charge can then be estimated concurrently. Experimental results show that the estimation accuracy of the proposed sequential algorithm is much better than the concurrent algorithm where all parameters/states are estimated simultaneously, and the computational cost can also be reduced. Finally, experiments are conducted under different temperatures to verify the effectiveness of the proposed algorithm for various battery *** Codes 93B99 Copyright © 2019, The Authors. All rights reserved.

关键词： State of charge

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Developing Automated Bifurcation Angle Measurement Algorithm for Retinal Image Analysis

Developing Automated Bifurcation Angle Measurement Algorithm...

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2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018

作者： Kipli, Kuryati Hamid, Isredza Rahmi A. Kipli, Kumalasari Ismail, Nur Afifah Zamhari, Nurdiani Mat, Dayang Azra Awang Suhaili, Shamsiah Sapawi, Rohana Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia Department of Information Security Faculty of Computer Sciences Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Architecture Planning and Surveying Universiti Teknologi MARA Sarawak Malaysia

ISBN: (纸本)9781538683699

Retina and its vascular network have unique branching characteristics morphology. The abnormalities in the vascular pattern of the retina, such as vessel shape, branching pattern, width, tortuosity, or the appearance of retinal itself, may be associated with the occurrence of retinopathy or cardiovascular diseases. This paper is focus on the development of an algorithm to measure bifurcation angle of the retinal for image analysis. From survey, there is still lack of automation for bifurcation angle measurement. Thus this initial work investigates and proposes the key steps for bifurcation measurement process. The key steps of the proposed method include segmentation of the region of interest (ROI), tracing the vessels that are needed for angle determination and lastly determine the bifurcation angle by using the dot product of the traced boundaries. The results are compared with the manual measurement of the bifurcation angle using quantitative and qualitative measurement. The experimental results show promising result of the proposed method in automating the measurement of bifurcation angle of retinal. Recommendation for room of improvement is also discussed in the paper. © 2018 IEEE.

关键词： Image analysis

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Towards spatial integration of qualitative data for urban transformation – challenges with automated geovisualization of perception of urban places

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IOP Conference Series: Earth and Environmental Science 2020年第5期588卷

作者： L Thuvander F Latino C Zangelidis M Adelfio V Naserentin A Logg Department of Architecture and Civil Engineering Chalmers University of Technology 412 96 Gothenburg Sven Hultins gata 6 Sweden Department of Mathematical Sciences Chalmers University of Technology 412 96 Gothenburg Chalmers tvärgata 3 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124 Thessaloniki Greece

Urban planning needs to face and integrate ecological, social, and economic aspects of city living. So far, attempts to integrate different urban simulation models into one technical platform have focused on quantitative data. The aim of this paper is to present the preparation for an automated method to spatially integrate and visualize interview-based qualitative data on the perception of urban places into a virtual platform. The Gothenburg suburb of Hammarkullen is used as a case study. Two CAQDAS software, NVivo and ***, were tested. In both software, locations and urban qualities were coded and clustered. Visualization strategies such as information tree structures, geo-coded quotations, spatial word clouds, linked to 2D maps and 3D environments were developed. Results identify the challenges to overcome and show limitations of the software in terms of creating graphs and relationships as well as automated geocoding of data to maps. The project offers a step towards the integration of qualitative (social) data into digital environments that can be scaled up. By that, it contributes with a new dimension of analysis of urban environments which is necessary for sustainable transformation of cities.

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Publisher Correction: Twisted moiré conductive thermal metasurface

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Nature communications 2024年第1期15卷 2876页

作者： Huagen Li Dong Wang Guoqiang Xu Kaipeng Liu Tan Zhang Jiaxin Li Guangming Tao Shuihua Yang Yanghua Lu Run Hu Shisheng Lin Ying Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China. Smart Materials for Architecture Research Lab Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China. State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China. Chongqing 2D Materials Institute Chongqing 400015 China. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. eleying@***. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. eleying@***. Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. chengwei.qiu@nus.edu.sg.

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Search for W′ bosons decaying to a top and a bottom quark in leptonic final states in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2024年第5期2024卷 1-51页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Van Mechelen, P. Bols, E.S. D’hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

A search for W′ bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb⁻¹ of proton-proton collisions at a center-of-mass energy of 13 TeV. Good agreement with the standard model expectation is observed and no evidence for the existence of the W′ boson is found over the mass range examined. The largest observed deviation from the standard model expectation is found for a W′ boson mass (mW_′) hypothesis of 3.8 TeV with a relative decay width of 1%, with a local (global) significance of 2.6 (2.0) standard deviations. Upper limits on the production cross sections of W′ bosons decaying to a top and a bottom quark are set. Left- and right-handed W′ bosons with mW_′ below 3.9 and 4.3 TeV, respectively, are excluded at the 95% confidence level, under the assumption that the new particle has a narrow decay width. Limits are also set for relative decay widths up to 30%. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Vector Boson Production

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Balanced Quantization： An Effective and Efficient Approach toQuantized Neural Networks

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Journal of computer Science & technology 2017年第4期32卷 667-682页

作者： Shu-Chang Zhou Yu-Zhi Wang He Wen Qin-Yao He Yu-Heng Zou University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Megvii Inc. Beijing 100190 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

Quantized neural networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in neural networks are often imbalanced, such that the uniform quantization determined from extremal values may underutilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both convolutional neural networks and recurrent neural networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7%, which is superior to the state-of-the-arts of QNNs.

关键词： quantized neural network percentile histogram equalization uniform quantization

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Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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Computing and Software for Big Science 2024年第1期8卷 1-36页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P.S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Mechelen, P. Van Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Faham, H. El Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Onsem, G. P. Van Putte, S. Van Vannerom, D. Clerbaux, B. Das, A.K. De Lentdecker, G. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F.A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Bossche, N. Van Den Linden, J. van der Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sh. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. De Souza, S. Fonseca De Souza, R. Gomes Martins, J. Herrera, C. Mora Mundim, L. Nogima, H. Pinheiro, J.P. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T. R. Fernandez Perez Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysic

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. © The Author(s) 2024.

关键词： CMS Machine learning Offline and computing

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