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.
As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classif...
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As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN *** existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force *** experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 ***-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).
Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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In the rapidly evolving landscape of cyber threats, phishing continues to be a prominent vector for cyberattacks, posing significant risks to individuals, organizations and information systems. This letter delves into...
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We propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of Rd. Our proposed...
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Hand gesture serves as a crucial role during the expression of sign language. Current deep learning based methods for sign language understanding (SLU) are prone to over-fitting due to insufficient sign data resource ...
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Robust fake speech detection systems are crucial in an era where audio recordings can be easily altered or developed due to advancements in technology. The potential impact of this technology could be devastating due ...
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Population growth in cities results in a demand for parking lots from an increasing number of automobiles, which frequently contributes to the global problem of traffic congestion. This study presents the smart parkin...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective ...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective evaluation ofMLmodel performances in property prediction of out-ofdistribution(OOD)materials that are different fromthe training *** performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets.
Combining optical and electronic systems could enable information processing that is a million times faster than existing gigahertz technology. Imagine leveraging nature’s fastest processes to power the electronics i...
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Combining optical and electronic systems could enable information processing that is a million times faster than existing gigahertz technology. Imagine leveraging nature’s fastest processes to power the electronics in semiconductor chips, quantum sensors and quantum computers. Such transformative speed would not only greatly improve the performance of technology, but unveil new vistas for fundamental science as well.
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