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.
In this paper, we studied the cooperative spectrum sensing (CSS) problem based on deep learning. The current CSS methods based on deep learning only extract general features of signal samples, without considering the ...
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Traditional method of images is routinely applied in electrostatic problems with infinite ground planes. However, practical situations almost always deal with finite ground planes. This causes the prediction to be ine...
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With the emergence of social networks and digital media, there has been an increasing proliferation of channels through which people receive information, potentially resulting in the widespread dissemination of fake n...
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This paper proposes a dual-mode Butler matrix based on the air suspended line (ASL) for millimeter-wave (mm-wave) dual-polarized antenna arrays. The ASL, which can simultaneously support TEM-mode and TE10-mode, is use...
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In the field of engineering design, there is a class of constrained multi-objective optimization problems where the optimal solutions are often found at the constraint boundaries. However, effectively utilizing the in...
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This article presents an output capacitor-less low dropout regulator (LDO) that achieves fast transient response through a current compensation circuit. The push-pull structure improves the voltage swing rate at the g...
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Integrated sensing and communication (ISAC) refers to a new information processing technology that achieves collaborative sensing and communication functions based on resource or information sharing between software a...
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Originally, borehole radar (BHR) is a subsurface geological structure detection and imaging tool. However, when borehole is filled with the very conductive medium, the radar wave will be attenuated seriously which res...
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Deep learning based recommender systems(DLRS) as one of the up-And-coming recommender systems, and their robustness is crucial for building trustworthy recommender systems. However, recent studies have demonstrated th...
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