The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Minimizing Computation in Convolutional Neural Networks
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Gaussian elimination is not optimal
Strassen, V. (1969) · Numerische Mathematik · doi:10.1007/bf02165411 · Cited by 2,509
A dynamically configurable coprocessor for convolutional neural networks
Chakradhar, S., Sankaradas, M., Jakkula, V., & Cadambi, S. (2010) · Proceedings of the 37th annual international symposium on Computer architecture · doi:10.1145/1815961.1815993 · Cited by 343
Cited by
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
“Other approaches include Strassen [75] and Winograd [76], which rearrange the computation such that the number of multiplications reduce from O(N(3)) to O(N) and by 2.”
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529
“More specifically, for an N × N input, K × K filters, R filters per layer, and L layers, the cost of evaluating the CNN grows like O(N2 K 2 R2 L) [58].”
BackgroundOptimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., & Cong, J. (2015) · Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/2684746.2689060 · Cited by 2,158
Self-citationShickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., & Sze, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001177 · Cited by 1,182
Metadata
- Authors
- Cong, J., & Xiao, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 281-290
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1005811612
- Cited by
- 372
- References
- 4
- FWCI
- 11.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Minimizing Computation in Convolutional Neural Networks. https://app.cochat.ai/papers/10.1007/978-3-319-11179-7_36
- The paper
- Cong, J., & Xiao, B. (2014). Minimizing Computation in Convolutional Neural Networks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-11179-7_36
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
