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melange:papers:fall2019 [2019/10/09 12:03]
jana
melange:papers:fall2019 [2019/11/04 17:19]
jana
Line 19: Line 19:
  
  
-@misc{kwon2018understanding+ 
-    title={Understanding Reuse, Performance,​ and Hardware Cost of DNN Dataflows: A Data-Centric Approach},​ +@article{DBLP:​journals/​corr/​abs-1805-02566
-    ​author={Hyoukjun Kwon and Prasanth Chatarasi and Michael Pellauer and Angshuman Parashar and Vivek Sarkar and Tushar Krishna}, +  ​author ​   = {Hyoukjun Kwon and 
-    year={2018},​ +               ​Michael Pellauer and 
-    ​eprint={1805.02566},​ +               ​Tushar Krishna}, 
-    archivePrefix={arXiv},​ +  ​title     ​= {Understanding Reuse, Performance,​ and Hardware Cost of DNN Dataflows: A Data-Centric Approach},​ 
-    ​primaryClass={cs.DC}+  ​journal ​  = {CoRR}, 
 +  ​volume ​   = {abs/​1805.02566},​ 
 +  ​year      = {2018}, 
 +  ​url ​      = {http://​arxiv.org/​abs/​1805.02566},​ 
 +  archivePrefix = {arXiv}, 
 +  ​eprint ​   = {1805.02566},​ 
 +  timestamp = {Mon, 13 Aug 2018 16:46:45 +0200}, 
 +  biburl ​   = {https://​dblp.org/​rec/​bib/​journals/​corr/​abs-1805-02566},​ 
 +  bibsource = {dblp computer science bibliography,​ https://​dblp.org}
 } }
 +
  
  
Line 46: Line 55:
  ​address = {New York, NY, USA},  ​address = {New York, NY, USA},
 } }
 +
 +@ARTICLE{7738524,​ author={Y. H. Chen and T. Krishna and J. S. Emer and V. Sze}, journal={IEEE Journal of Solid-State Circuits}, title={Eyeriss:​ An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks}, year={2017},​ volume={52},​ number={1}, pages={127-138},​ url = {http://​ieeexplore.ieee.org/​document/​7738524/​},​ doi={10.1109/​JSSC.2016.2616357},​ ISSN={0018-9200},​ month={Jan},​} ​
 +
 +@article{Vasilache:​2019:​NAL:​3366460.3355606,​
 + ​author = {Vasilache, Nicolas and Zinenko, Oleksandr and Theodoridis,​ Theodoros and Goyal, Priya and Devito, Zachary and Moses, William S. and Verdoolaege,​ Sven and Adams, Andrew and Cohen, Albert},
 + title = {The Next 700 Accelerated Layers: From Mathematical Expressions of Network Computation Graphs to Accelerated GPU Kernels, Automatically},​
 + ​journal = {ACM Trans. Archit. Code Optim.},
 + ​issue_date = {October 2019},
 + ​volume = {16},
 + ​number = {4},
 + month = oct,
 + year = {2019},
 + issn = {1544-3566},​
 + pages = {38:​1--38:​26},​
 + ​articleno = {38},
 + ​numpages = {26},
 + url = {http://​doi.acm.org/​10.1145/​3355606},​
 + doi = {10.1145/​3355606},​
 + acmid = {3355606},
 + ​publisher = {ACM},
 + ​address = {New York, NY, USA},
 + ​keywords = {Deep learning layers, GPU acceleration,​ polyhedral compilation},​
 +
melange/papers/fall2019.txt · Last modified: 2019/12/02 11:09 by jana