ic-lab-duth/FusedGCN4HLS
Systolic Three Matrix Multiplier for Graph Convolutional Networks using High Level Synthesis
This project offers a specialized computational architecture designed to speed up how quickly Graph Convolutional Networks (GCNs) can analyze graph-structured data. It takes your graph's connections (adjacency matrix) and node information (feature matrix), along with learned network weights, to quickly produce updated node representations. This is for machine learning engineers or researchers working with GCNs to process large graph datasets more efficiently.
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Use this if you need to accelerate the inference speed of Graph Convolutional Networks, especially for large graph-structured datasets like those found in social networks or recommendation systems.
Not ideal if you are developing new GCN models from scratch or if your primary need is for training GCNs rather than just performing inference.
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23
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Language
C++
License
MIT
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Last pushed
Jul 29, 2022
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