Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment
J. Wu, L. Wang, Q. Jin, F. Liu, IEEE Transactions on Parallel and Distributed Systems 35 (2023) 280–296.
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Journal Article
| Published
| English
Author
Wu, Jing;
Wang, LinLibreCat
;
Jin, Qirui;
Liu, Fangming
Department
Publishing Year
Journal Title
IEEE Transactions on Parallel and Distributed Systems
Volume
35
Issue
2
Page
280-296
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Wu J, Wang L, Jin Q, Liu F. Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment. IEEE Transactions on Parallel and Distributed Systems. 2023;35(2):280-296. doi:10.1109/tpds.2023.3340518
Wu, J., Wang, L., Jin, Q., & Liu, F. (2023). Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment. IEEE Transactions on Parallel and Distributed Systems, 35(2), 280–296. https://doi.org/10.1109/tpds.2023.3340518
@article{Wu_Wang_Jin_Liu_2023, title={Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment}, volume={35}, DOI={10.1109/tpds.2023.3340518}, number={2}, journal={IEEE Transactions on Parallel and Distributed Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Wu, Jing and Wang, Lin and Jin, Qirui and Liu, Fangming}, year={2023}, pages={280–296} }
Wu, Jing, Lin Wang, Qirui Jin, and Fangming Liu. “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment.” IEEE Transactions on Parallel and Distributed Systems 35, no. 2 (2023): 280–96. https://doi.org/10.1109/tpds.2023.3340518.
J. Wu, L. Wang, Q. Jin, and F. Liu, “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment,” IEEE Transactions on Parallel and Distributed Systems, vol. 35, no. 2, pp. 280–296, 2023, doi: 10.1109/tpds.2023.3340518.
Wu, Jing, et al. “Graft: Efficient Inference Serving for Hybrid Deep Learning With SLO Guarantees via DNN Re-Alignment.” IEEE Transactions on Parallel and Distributed Systems, vol. 35, no. 2, Institute of Electrical and Electronics Engineers (IEEE), 2023, pp. 280–96, doi:10.1109/tpds.2023.3340518.