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Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
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PG-KINN outperforms legacy approaches by leveraging a Petrov-Galerkin framework, achieving robust solutions for complex PDEs with improved accuracy and interpretability.
Achieving up to 28% power reduction, this new multiplier redefines efficiency in modular arithmetic for RNS, crucial for high-performance computing tasks.
