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Found 3 papers, 2 papers with code
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
To enable the base model permutation equivariant, we integrate it with graph topology and node features separately, resulting in local and global equivariant attention models.
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Node Classification
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Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner.
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Experimental results demonstrate our approach can achieve up to 13. 4x speedup when compared to previous FPGA-based accelerators for the BERT model.
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