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 solutio...
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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.
The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources,...
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The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or ...
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The Computational Visual Media(CVM)conference series is intended to provide a prominent international forum for exchanging innovative research ideas and significant computational methodologies that either underpin or apply visual media.
High Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM) are two important branches in the data mining area, where Frequent Itemset Mining considers itemsets that occur in large numbers in the transaction ...
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This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in le...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict softwa...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data re-weighting, etc. However, most of these methods do not factor in variations in learning dif...
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Small UAVs pose security risks to sensitive areas and individuals due to their rapid movement and wide coverage capabilities. Effective monitoring necessitates the deployment of lightweight and energy-efficient survei...
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Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. T...
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This research introduces a unique approach to segmenting breast cancer images using a U-Net-based ***,the computational demand for image processing is very ***,we have conducted this research to build a system that en...
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This research introduces a unique approach to segmenting breast cancer images using a U-Net-based ***,the computational demand for image processing is very ***,we have conducted this research to build a system that enables image segmentation training with low-power *** accomplish this,all data are divided into several segments,each being trained *** the case of prediction,the initial output is predicted from each trained model for an input,where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs,which also ensures data *** addition,this kind of distributed training system allows different computers to be used *** is how the training process takes comparatively less time than typical training *** after completing the training,the proposed prediction system allows a newly trained model to be included in the ***,the prediction is consistently more *** evaluated the effectiveness of the ultimate output based on four performance matrices:average pixel accuracy,mean absolute error,average specificity,and average balanced *** experimental results show that the scores of average pixel accuracy,mean absolute error,average specificity,and average balanced accuracy are 0.9216,0.0687,0.9477,and 0.8674,*** addition,the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational *** it outperformed all of them in these aspects.
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