检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Lin, Ning Wang, Shaocong Li, Yi Wang, Bo Shi, Shuhui He, Yangu Zhang, Woyu Yu, Yifei Zhang, Yue Zhang, Xinyuan Wong, Kwunhang Wang, Songqi Chen, Xiaoming Jiang, Hao Zhang, Xumeng Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Liu, Ming Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits and Key Laoratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics the Chinese Academy of Sciences Beijing100029 China The School of Microelectronics Southern University of Science and Technology Shenzhen518055 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore’s law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware. © 2023, CC BY-NC-SA.

关键词： Image enhancement

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Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics

arXiv

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arXiv 2024年

作者： Prokopenko, Mikhail Davies, Paul C.W. Harré, Michael Heisler, Marcus Kuncic, Zdenka Lewis, Geraint F. Livson, Ori Lizier, Joseph T. Rosas, Fernando E. The Centre for Complex Systems University of Sydney SydneyNSW2006 Australia School of Computer Science Faculty of Engineering University of Sydney SydneyNSW2006 Australia The Beyond Center for Fundamental Concepts in Science Arizona State University TempeAZ85287–0506 United States School of Life and Environmental Sciences Faculty of Science University of Sydney SydneyNSW2006 Australia School of Physics Faculty of Science University of Sydney SydneyNSW2006 Australia The Charles Perkins Centre University of Sydney SydneyNSW2006 Australia Sussex AI Centre for Consciousness Science Department of Informatics University of Sussex BrightonBN19RH United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London LondonSW72AZ United Kingdom Centre for Complexity Science Imperial College London LondonSW72AZ United Kingdom Center for Eudaimonia and Human Flourishing University of Oxford OxfordOX39BX United Kingdom

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism’s components, leading to self-modelling "tangled hierarchies". Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the Gödel–Turing–Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of *** Codes 03Dxx, 68Qxx, 92Dxx, 37N25 Copyright © 2024, The Authors. All rights reserved.

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A Systematic Assessment of Openai o1-Preview for Higher Order Thinking in Education

arXiv

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arXiv 2024年

作者： Latif, Ehsan Zhou, Yifan Guo, Shuchen Gao, Yizhu Shi, Lehong Nyaaba, Matthew Lee, Gyeonggeon Zhang, Liang Bewersdorff, Arne Fang, Luyang Yang, Xiantong Zhao, Huaqin Jiang, Hanqi Lu, Haoran Li, Jiaxi Yu, Jichao Wu, Xuansheng You, Weihang Liu, Zhengliang Liu, Vincent Shung Wang, Hui Wu, Zihao Lu, Jin Dou, Fei Ma, Ping Liu, Ninghao Liu, Tianming Zhai, Xiaoming AI4STEM Education Center University of Georgia AthensGA United States National GENIUS Center AthensGA United States School of Computing University of Georgia AthensGA United States Department of Electrical Engineering and Computer Engineering University of Memphis MemphisTN United States Department of Natural Sciences and Science Education National Institute of Education Singapore Singapore School of Social Sciences and Technology Technical University of Munich Munich Germany Department of Statistics University of Georgia AthensGA United States Faculty of Psychology Beijing Normal University Beijing China Second Language Acquisition and Teaching University of Arizona TucsonAZ United States Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States Department of Workforce Education and Instructional Technology University of Georgia AthensGA United States School of Teacher Education Nanjing Normal University Jiangsu Nanjing China

As artificial intelligence (ai) continues to advance, it demonstrates capabilities comparable to human intelligence, having significant potential to revolutionize education and workforce development. To assess the state-of-art ai capacity, this study provides a comprehensive evaluation of Openai o1-preview’s ability to perform higher-order cognitive tasks across 14 distinct dimensions, including critical thinking, systems thinking, computational thinking, design thinking metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. In each dimension, we adopt existing instruments with high reliability and validity, such as the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to systematically assess o1-preview’s higher-order thinking in comparison to human performance. We found overall outperformance of o1-preview for most of the categories: • o1-preview achieved significantly outperform (150%) as compared to humans for systematic thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. • For logical reasoning, critical thinking, and quantitative reasoning, o1-preview slightly under-performed by 25% than human such as for logical reasoning. • For analogical reasoning, we found that both human and o1-preview achieve perfect scores for each given task. Nevertheless, we identify o1-preview’s limited capacity in thinking such as abstract reasoning, where humans (i.e., psychology students) perform better than o1-preview, indicating that human oversight remains crucial for tasks requiring high-level abstraction. The findings have broad implications for education, advocating a shift in educational paradigms to emphasize the development of complementary human skills, such as creativity, abstract reasoning, and critical thinking. This study highlights the transformative role of ai in educati

关键词： Systems thinking

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Cryogenic quasi-static embedded DRAM for energy-efficient compute-in-memory applications

arXiv

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arXiv 2023年

作者： Shu, Yuhao Zhang, Hongtu Sun, Hao Zhang, Mengru Zhao, Wenfeng Deng, Qi Tang, Zhidong Yuan, Yumeng Hu, Yongqi Gu, Yu Kou, Xufeng Ha, Yajun School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China State Key Laboratory of Integrated Chip and System Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Department of Electrical and Computer Engineering Binghamton University SUNY BinghamtonNY United States Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Engineering Research Center of Energy Efficient and Custom AI IC Shanghai China

Compute-in-memory (CIM) presents an attractive approach for energy-efficient computing in data-intensive applications. However, the development of suitable memory designs to achieve high-performance CIM remains a challenging task. Here, we propose a cryogenic quasi-static embedded DRAM to address the logic-memory mismatch of CIM. Guided by the re-calibrated cryogenic device model, the designed four-transistor bit-cell achieves full-swing data storage, low power consumption, and extended retention time at cryogenic temperatures. Combined with the adoption of cryogenic write bitline biasing technique and readout circuitry optimization, our 4Kb cryogenic eDRAM chip demonstrates a 1.37×106 times improvement in retention time, while achieving a 75 times improvement in retention variability, compared to room-temperature operation. Moreover, it also achieves outstanding power performance with a retention power of 112 fW and a dynamic power of 108 µW at 4.2 K, which can be further decreased by 7.1% and 13.6% using the dynamic voltage scaling technique. This work reveals the great potential of cryogenic CMOS for high-density data storage and lays a solid foundation for energy-efficient CIM implementations. Copyright © 2023, The Authors. All rights reserved.

关键词： Computation theory

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Mitosis domain generalization in histopathology images - The MIDOG challenge

arXiv

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arXiv 2022年

作者： Aubreville, Marc Stathonikos, Nikolas Bertram, Christof A. Klopfleisch, Robert ter Hoeve, Natalie Ciompi, Francesco Wilm, Frauke Marzahl, Christian Donovan, Taryn A. Maier, Andreas Breen, Jack Ravikumar, Nishant Chung, Youjin Park, Jinah Nateghi, Ramin Pourakpour, Fattaneh Fick, Rutger H.J. Hadj, Saima Ben Jahanifar, Mostafa Rajpoot, Nasir Dexl, Jakob Wittenberg, Thomas Kondo, Satoshi Lafarge, Maxime W. Koelzer, Viktor H. Liang, Jingtang Wang, Yubo Long, Xi Liu, Jingxin Razavi, Salar Khademi, April Yang, Sen Wang, Xiyue Veta, Mitko Breininger, Katharina Technische Hochschule Ingolstadt Ingolstadt Germany Pathology Department UMC Utrecht Netherlands Institute of Pathology University of Veterinary Medicine Vienna Austria Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Computational Pathology Group Radboud UMC Nijmegen Netherlands Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Anatomic Pathology Schwarzman Animal Medical Center New York United States CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds United Kingdom Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Electrical and Electronics Engineering Department Shiraz University of Technology Shiraz Iran Tehran Iran Tribun Health Paris France Tissue Image Analytics Centre Department of Computer Science University of Warwick United Kingdom Fraunhofer-Institute for Integrated Circuits IIS Erlangen Germany Muroran Institute of Technology Hokkaido Japan Department of Pathology and Molecular Pathology University Hospital University of Zurich Zurich Switzerland School of Life Science and Technology Xidian University Shannxi China Histo Pathology Diagnostic Center Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Tencent AI Lab Shenzhen518057 China College of Computer Science Sichuan University Chengdu610065 China Medical Image Analysis Group TU Eindhoven Netherlands Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCai MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Molecular geometry prediction using a deep generative graph neural network

arXiv

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arXiv 2019年

作者： Mansimov, Elman Mahmood, Omar Kang, Seokho Cho, Kyunghyun Department of Computer Science Courant Institute of Mathematical Sciences New York University 60 5th Avenue New YorkNY10011 United States Center for Data Science New York University 60 5th Avenue New YorkNY10011 United States Department of Systems Management Engineering Sungkyunkwan University 2066 Seobu-ro Jangan-gu Suwon16419 Korea Republic of Facebook AI Research 770 Broadway New YorkNY10003 United States CIFAR Azrieli Global Scholar Canadian Institute for Advanced Research 661 University Avenue TorontoONM5G 1M1 Canada

A molecule’s geometry, also known as conformation, is one of a molecule’s most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations. Copyright © 2019, The Authors. All rights reserved.

关键词： Molecules

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Industry-Scale Orchestrated Federated Learning for Drug Discovery 37

Industry-Scale Orchestrated Federated Learning for Drug Disc...

引用

37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Oldenhof, Martijn Ács, Gergely Pejó, Balázs Schuffenhauer, Ansgar Holway, Nicholas Sturm, Noé Dieckmann, Arne Fortmeier, Oliver Boniface, Eric Mayer, Clément Gohier, Arnaud Schmidtke, Peter Niwayama, Ritsuya Kopecky, Dieter Mervin, Lewis Rathi, Prakash Chandra Friedrich, Lukas Formanek, András Antal, Peter Rahaman, Jordon Zalewski, Adam Heyndrickx, Wouter Oluoch, Ezron Stößel, Manuel Vančo, Michal Endico, David Gelus, Fabien de Boisfossé, Thaïs Darbier, Adrien Nicollet, Ashley Blottière, Matthieu Telenczuk, Maria Nguyen, Van Tien Martinez, Thibaud Boillet, Camille Moutet, Kelvin Picosson, Alexandre Gasser, Aurélien Djafar, Inal Simon, Antoine Arany, Ádám Simm, Jaak Moreau, Yves Engkvist, Ola Ceulemans, Hugo Marini, Camille Galtier, Mathieu KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 Heverlee3001 Belgium BME-HIT CrySyS Lab Budapest Hungary BME-MIT Budapest Hungary Novartis Institutes for BioMedical Research Novartis Campus BaselCH-4002 Switzerland Bayer AG Leverkusen51373 Germany Substra Foundation - Labelia Labs Nantes France Discngine 79 Avenue Ledru Rollin Paris75012 France Institut de Recherches Servier 125 chemin de ronde Croissy-sur-Seine78290 France Boehringer Ingelheim RCV GmbH & Co KG Vienna Austria Molecular AI Discovery Sciences R&D AstraZeneca Cambridge United Kingdom R&D IT AstraZeneca Cambridge United Kingdom Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Merck KGaA Global Research & Development Frankfurter Strasse 250 Darmstadt64293 Germany GmbH Staffelseestraße 2 Munich81477 Germany Pillar Biosciences Inc. 9 Strathmore Rd NatickMA01760 United States Janssen Pharmaceutica NV Turnhoutseweg 30 Beerse2340 Belgium Kubermatic Hamburg Germany Owkin Paris France

ISBN: (纸本)9781577358800

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Industrial research

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

引用

arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCai) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCai 2017), and 0.739 (MICCai 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCai 2017), and 0.554 (MICCai 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Record Acceleration of the Two-Dimensional Ising Model Using a High-Performance Wafer-Scale Engine

SSRN

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SSRN 2024年

作者： Shi, Wei Van Essendelft, Dirk Saidi, Wissam Abdo Almolyki, Hayl Jordan, Terry Wang, Mei-Yu The National Energy Technology Laboratory 3610 Collins Ferry Rd MorgantownWV26505 United States Department of Computer Science and Engineering Tandon School of Engineering New York University 6 MetroTech Center BrooklynNY11201 United States The National Energy Technology Laboratory 626 Cochrans Mill Rd PittsburghPA15236 United States HPC AI and Big Data Group Pittsburgh Supercomputing Center Carnegie Mellon University 300 S. Craig St. PittsburghPA15213 United States The National Energy Technology Laboratory 626 Cochrans Mill Rd PittsburghPA15236 United States

The versatility and wide-ranging applicability of the Ising model, originallyintroduced to study phase transitions in magnetic materials, have made it a cornerstonein statistical physics and a valuable tool for evaluating the performanceof emerging computer hardware. Here, we present a novel implementation of thetwo-dimensional Ising model on Cerebras Wafer-Scale Engine (WSE) – a revolutionaryprocessor that is opening new frontiers in computing. In our deploymentof the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representationstoring 16 spins on each int16 word, and efficiently distributed the spinsover the processing units enabling seamless weak scaling and limiting communicationsto only immediate neighboring units. Our implementation can handleup to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flipattempts per second for Ising models with up to 200 million spins. This representsa gain of up to 148 times over previously reported single-devices with a highlyoptimized implementation on NVIDIA V100 and up to 88 times in productivitycompared to NVIDIA H100. Our findings highlight the significant potential ofthe WSE in scientific computing, particularly in the field of materials modeling. © 2024, The Authors. All rights reserved.

关键词： Ising model

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Retraction Note: An effective deep learning features based integrated framework for iris detection and recognition

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 217-217页

作者： Jayanthi, J. Lydia, E. Laxmi Krishnaraj, N. Jayasankar, T. Babu, R. Lenin Suji, R. Adaline Department of CSE Sona College of Technology Salem India Department of CSE Vignan’s Institute of Information Technology Visakhapatnam India Sasi Institute of Technology & Engineering Tadepalligudem India Electronics and Communication Engineering Department University College of Engineering Anna University Tiruchirappalli India ConverSight.ai Indianapolis USA Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Coimbatore India

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