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Found 3 papers, 1 papers with code
SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification
Thus, we design the sparse MHA module, which computes and stores only large attention weights to reduce memory consumption, and the routed FFN module, which dynamically activates a subset of model parameters for each token to reduce computation cost.
DGI: Easy and Efficient Inference for GNNs
In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution.
Vertex-Centric Visual Programming for Graph Neural Networks
Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering.
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