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ReLaX-Net: Reusing Layers for Parameter-Efficient Physical Neural Networks

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent introduction of ReLaX-Net marks a significant advancement in the field of Physical Neural Networks (PNNs), which are seen as the future of computing. This innovative approach focuses on reusing layers to enhance parameter efficiency, addressing the current limitations of PNNs compared to their digital counterparts. As digital neural networks have rapidly evolved, PNNs have struggled to keep pace, but ReLaX-Net could bridge this gap, making PNNs more competitive and paving the way for next-generation computing systems. This development is crucial as it could lead to more efficient and powerful computing technologies.
— Curated by the World Pulse Now AI Editorial System
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