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University Duisburg-Essen
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KANLib achieves competitive efficiency and flexibility in KAN architectures, enabling researchers to explore innovative designs with minimal performance trade-offs.
Fixed-reference delta compression achieves nearly 50% weight reduction while only sacrificing 8.3% accuracy, revolutionizing DNN deployment on FPGAs.
Stop wrestling with brittle, platform-specific NAS pipelines: elasticAI.explorer offers a unified, extensible framework for hardware-aware neural architecture search across diverse accelerators.
