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Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
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Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
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Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
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EIE: Efficient inference engine on compressed deep neural network
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., and Dally, W. J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K · 2016
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Composing graphical models with neural networks for structured representations and fast inference
Johnson, M. J., Duvenaud, D. K., Wiltschko, A., Adams, R. P., and Datta, S. R · 2016
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Glow: Graph lowering compiler techniques for neural networks
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Dynamic control flow in large-scale machine learning
Yu, Y., Abadi, M., Barham, P., Brevdo, E., Burrows, M., Davis, A., Dean, J., Ghemawat, S., Harley, T., Hawkins, P., et al · 2018
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Semantic object parsing with graph lstm
Liang, X., Shen, X., Feng, J., Lin, L., and Yan, S · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Deep learning with dynamic computation graphs
Looks, M., Herreshoff, M., Hutchins, D., and Norvig, P · 2017
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Dynet: The dynamic neural network toolkit
Neubig, G., Dyer, C., Goldberg, Y., Matthews, A., Ammar, W., Anastasopoulos, A., Ballesteros, M., Chiang, D., Clothiaux, D., Cohn, T., et al · 2017
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Xla - tensorflow, compiled, March 2017
XLA Team · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Learning to optimize halide with tree search and random programs
Adams, A., Ma, K., Anderson, L., Baghdadi, R., Li, T.-M., Gharbi, M., Steiner, B., Johnson, S., Fatahalian, K., Durand, F., et al · 2019
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JANUS: Fast and flexible deep learning via symbolic graph execution of imperative programs
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Optimizing CNN model inference on cpus
Liu, Y., Wang, Y., Yu, R., Li, M., Sharma, V., and Wang, Y · 2019
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PyTorch: An imperative style, high-performance deep learning library
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Relay: A high-level ir for deep learning
Roesch, J., Lyubomirsky, S., Kirisame, M., Pollock, J., Weber, L., Jiang, Z., Chen, T., Moreau, T., and Tatlock, Z · 2019
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A unified optimization approach for cnn model inference on integrated gpus
Wang, L., Chen, Z., Liu, Y., Wang, Y., Zheng, L., Li, M., and Wang, Y · 2019
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Openblas
Zhang, X., Wang, Q., and Chothia, Z · 2019
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Intel® math kernel library for deep learning networks, 2020
Intel · 2021
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Mlir: Scaling compiler infrastructure for domain specific computation
Lattner, C., Amini, M., Bondhugula, U., Cohen, A., Davis, A., Pienaar, J. A., Riddle, R., Shpeisman, T., Vasilache, N., and Zinenko, O · 2021
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