The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Hardware accelerated convolutional neural networks for synthetic vision systems
Abstract
In this paper we present a scalable hardware architecture to implement large-scale convolutional neural networks and state-of-the-art multi-layered artificial vision systems. This system is fully digital and is a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images. We present a performance comparison between a software, FPGA and ASIC implementation that shows a speed up in custom hardware implementations.
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Cites
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
Self-citationBeyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Self-citationLearning methods for generic object recognition with invariance to pose and lighting
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Self-citation
Research works printed but not linked: 5
- 10.1109/TPAMI.2007.56 · DOI · Paper page
- 10.1109/CVPR.2007.383157 · DOI · Paper page
- 10.1109/FPL.2009.5272559 · DOI · Paper page
- 10.1109/72.129422 · DOI
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Cited by
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Self-citationShiDianNao: shifting vision processing closer to the sensor
Du, Z., Fasthuber, R., Chen, T., Ienne, P., Li, L., Luo, T., Feng, X., Chen, Y., & Temam, O. (2015) · Proceedings of the 42nd Annual International Symposium on Computer Architecture · doi:10.1145/2749469.2750389 · Cited by 933
Traffic sign recognition with multi-scale Convolutional Networks
Sermanet, P., & LeCun, Y. (2011) · The 2011 International Joint Conference on Neural Networks · doi:10.1109/ijcnn.2011.6033589 · Cited by 759
Self-citationThroughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
Suda, N., Chandra, V., Dasika, G., Mohanty, A., Ma, Y., Vrudhula, S., Seo, J.-S., & Cao, Y. (2016) · Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/2847263.2847276 · Cited by 553
Deep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
Metadata
- Authors
- Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., & Culurciello, E.
- Venue
- Proceedings of 2010 IEEE International Symposium on Circuits and Systems
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 257-260
- OpenAlex
- W1968422655
- Cited by
- 231
- References
- 4
- FWCI
- 6.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., & Culurciello, E. (2010). Hardware accelerated convolutional neural networks for synthetic vision systems. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. https://doi.org/10.1109/iscas.2010.5537908
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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.
