Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Modeling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering
Reference21 articles.
1. Ragan-Kelley, J., Adams, A., Paris, S., Levoy, M., Amarasinghe, S., & Durand, F. (2012). Decoupling algorithms from schedules for easy optimization of image processing pipelines.
2. Ragan-Kelley, J., Barnes, C., Adams, A., Paris, S., Durand, F., & Amarasinghe, S. (2013). Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. ACM SIGPLAN Notices, 48(6), 519–530.
3. Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. Preprint retrieved from http://arxiv.org/abs/1607.01759
4. Mikolov, T., Chen, K., Corrado, G. S., & Dean, J. (2013). Efficient estimation of word representations in vector space. CoRR. Preprint retrieved from http://arxiv.org/abs/1301.3781
5. Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532–1543).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. FSpGEMM: A Framework for Accelerating Sparse General Matrix–Matrix Multiplication Using Gustavson’s Algorithm on FPGAs;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-04
2. Support of Sparse Tensor Computing for MLIR HLS;Proceedings of the 52nd International Conference on Parallel Processing Workshops;2023-08-07
3. Accelerating Convolutional Neural Network by Exploiting Sparsity on GPUs;ACM Transactions on Architecture and Code Optimization;2023-07-19