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Article summary:
1. Graph Convolutional Networks (GCNs) are challenging to accelerate due to their large size, high sparsity, and irregular non-zero distribution.
2. The proposed hardware accelerator, called “I-GCN”, significantly improves data locality and reduces unnecessary computation through a new online graph restructuring algorithm called “islandization”.
3. Experimental results show that the proposed accelerator can achieve performance speedups of 5549×, 403×, and 5.7× on average over CPUs, GPUs, and prior art GCN accelerators respectively.
Article analysis:
The article is generally reliable in its claims about the effectiveness of the proposed hardware accelerator for GCN inference. The authors provide evidence from experiments that demonstrate the improved performance of their proposed solution compared to existing solutions such as CPUs and GPUs. The article also provides a detailed description of the islandization algorithm which is used to improve data locality and reduce unnecessary computation in the proposed accelerator.
However, there are some potential biases in the article that should be noted. For example, while the authors do mention some existing solutions such as CPUs and GPUs for comparison purposes, they do not discuss any other potential alternatives or counterarguments that could be made against their proposed solution. Additionally, there is no discussion of any possible risks associated with using this new hardware accelerator or any potential drawbacks that could arise from its use. Furthermore, it is unclear whether the experiments conducted by the authors were done under controlled conditions or if they took into account any external factors that could have affected their results.