DRGN: a dynamically reconfigurable accelerator for graph neural networks
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-022-04402-x.pdf
Reference51 articles.
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2. Auten A, Tomei M, Kumar R (2020) Hardware acceleration of graph neural networks. In: Paper presented at the 2020 57th ACM/IEEE design automation conference (DAC)
3. Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R et al (2011) Graph structure in the web. In: The structure and dynamics of networks. Princeton University Press, Princeton, p 183–194
4. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2015) Compound rank-k projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513
5. Chen K, Yao L, Zhang D, Wang X, Chang X, Nie F (2019) A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans Neural Netw Learn Syst 31(5):1747–1756
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