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Large circuit models: opportunities and challenges

science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Lung Cancer Classification from CT Scan Images Using the LVQ Algorithm and GLCM Feature Extraction with Spatial Filters

Lung Cancer Classification from CT Scan Images Using the LVQ...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Ahmad, Imam Tri Prastowo, Agung Indra Borman, Rohmat Tonggiroh, Mursalim Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Yapis Papua Faculty of Computer Science Jayapura Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Lung cancer is the leading cause of cancer-related fatalities in Indonesia, primarily due to late-stage diagnoses. This study aims to develop a model that employs image processing to classify lung cancer from CT scan images. The system combines the Learning Vector Quantization (LVQ) algorithm with the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and utilizes spatial filtering techniques such as low pass, median, and high pass filters. The LVQ algorithm effectively manages non-linear data and forms complex decision boundaries through the distribution of prototype vectors. The findings show that the median filtering technique achieved the highest accuracy of 90.14% at a GLCM angle of 0°, demonstrating its effectiveness in reducing noise while maintaining crucial details for accurate classification. This suggests that the horizontal texture orientation may be more critical for analyzing lung images in this dataset. © 2024 IEEE.

关键词： Lung cancer

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Attention Mechanism with Bayesian Decision Theory for Drug Prediction

Attention Mechanism with Bayesian Decision Theory for Drug P...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Biomedical literature lacks integration of dynamic drug interaction data for personalized prescriptions. This proposed work is a new hybrid model that enhances drug safety prescriptions by taking into account individual patient features and drug interactions. It does this by hybrid attention mechanisms with Bayesian Decision Theory (BDT), which employs Bayesian statistical thinking instead of the dot-product approach. This approach is driven by the growing complexity of patient profiles and the limitations of current algorithms, which often fail to dynamically change the relevance of individual aspects. This method seeks 94% accuracy, effectively prioritizing safer drug combinations and personalizing drug recommendations. This integration leads to more consistent and customized drug prescriptions, which ultimately improve patient outcomes and advance the field of personalized healthcare. © 2024 IEEE.

关键词： Drug interactions

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ML Driven Priority-Based Constrained Routing for Pollution Management in ITS 11

ML Driven Priority-Based Constrained Routing for Pollution M...

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Hegazy, Aly Soubra, Hassan Aboulyousr, Youssef German University in Cairo Faculty of Computer Science and Engineering Cairo Egypt ECE Faculty of Computer Science and Engineering France

ISBN: (纸本)9798350322101

Intelligent Transportation Systems (ITS) seek to enhance traffic safety, mobility, and efficiency through advanced technologies. Priority assignment, a fundamental component of ITS, allocates priority to various types of vehicles or roads based on their significance and urgency. In this study, we explore the implementation of priority-based constrained routing for road vehicles in ITS, leveraging machine learning techniques and dynamic routing algorithms. Our objective is to enhance traffic management and efficiency by addressing the limitations of traditional routing methods that do not account for vehicle priorities. By employing machine learning models within a priority-based routing system, dynamic priorities can be accurately assigned to vehicles in real-time, facilitating more effective traffic management decisions. This approach mitigates congestion and reduces travel times for high-priority vehicles, presenting an innovative means of refining traffic control strategies. The findings hold substantial implications for real-world ITS systems, as they can alleviate traffic congestion, improve traffic flow, enhance efficiency, and manage pollution levels in specific areas. © 2023 IEEE.

关键词： Smart city

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Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh

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Data Intelligence 2024年第3期6卷 834-868页

作者： M.Raihan Sami Azam Laboni Akter Md.Mehedi Hassan Ryana Quadir Asif Karim Saikat Mondal Arun More Department of Computer Science and Engineering North Western UniversityKhulna 9100Bangladesh Faculty of Science and Technology Charles Darwin UniversityNT 0909Australia Departmentof Biomedical Engineering Khulna University of Engineering&TechnologyKhulnaBangladeh Computer Science and Engineering Discipline Khulna UniversityKhulna 9208Bangladesh Department of Computer Science and Engineering Daffodil International University(DIU)Bangladesh Department of Cardiology Ter Institute of Rural Health and ResearchMurud-413510India

Ischemic heart disease(IHD)is one of the leading causes of death ***,different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of *** study examines the current IHD condition in southern Bangladesh,a Southeast Asian middle-income *** main approach to this research is an Al-based proposal of a reduced set of the greatest impact clinical traits that may cause *** approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical ***,diagnostic,and symptomatic features were considered for analysing this clinical *** pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations.A proposed voting mechanism ranked the selected 138 features by their impact *** this regard,diverse patterns in correlations with variables,including age,sex,career,family history,obesity,etc.,were calculated and explained in terms of voting *** the 138 risk factors,three labels were categorized:high-risk,medium-risk,and low-risk features;19 features were regarded as high,25 were medium,and 94 were considered low impactful *** research's technological methodology and practical goals provide an innovative and resilient framework for addressing IHD,especially in less developed cities and townships of Bangladesh,where the general population's socioeconomic conditions are often *** data collection,pre-processing,and use of this study's complete and comprehensive IHD patient dataset is another innovative *** believe that other relevant research initiatives will benefit from this work.

关键词： Ischemic heart disease Machine learning CVD Data categorization Medical data

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Feature engineering Methods for Analyzing Blood Samples for Early Diagnosis of Hepatitis Using Machine Learning Approaches

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computer Modeling in engineering & sciences 2025年第3期142卷 3229-3254页

作者： Mohamed A.G.Hazber Ebrahim Mohammed Senan Hezam Saud Alrashidi Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHail81481Saudi Arabia Department of Computer Science College of Applied SciencesHajjah UniversityHajjah9677Yemen Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyAl-Razi UniversitySana’a9671Yemen

Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions,and it has many types,from normal to *** is diagnosed through many blood tests and factors;Artificial Intelligence(AI)techniques have played an important role in early diagnosis and help physicians make *** study evaluated the performance of Machine Learning(ML)algorithms on the hepatitis data *** dataset contains missing values that have been processed and outliers *** dataset was counterbalanced by the Synthetic Minority Over-sampling Technique(SMOTE).The features of the data set were processed in two ways:first,the application of the Recursive Feature Elimination(RFE)algorithm to arrange the percentage of contribution of each feature to the diagnosis of hepatitis,then selection of important features using the t-distributed Stochastic Neighbor Embedding(t-SNE)and Principal Component Analysis(PCA)***,the SelectKBest function was applied to give scores for each attribute,followed by the t-SNE and PCA ***,the classification algorithms K-Nearest Neighbors(KNN),Support Vector Machine(SVM),Artificial Neural Network(ANN),Decision Tree(DT),and Random Forest(RF)were fed by the dataset after processing the features in different methods are RFE with t-SNE and PCA and SelectKBest with t-SNE and PCA).All algorithms yielded promising results for diagnosing hepatitis data *** RF with RFE and PCA methods achieved accuracy,Precision,Recall,and AUC of 97.18%,96.72%,97.29%,and 94.2%,respectively,during the training *** the testing phase,it reached accuracy,Precision,Recall,and AUC by 96.31%,95.23%,97.11%,and 92.67%,respectively.

关键词： Hepatitis machine learning PCA RFE SelectKBest t-SNE

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A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

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Frontiers of computer science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

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