The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs
Abstract
Convolutional Neural Networks (ConvNets) are a powerful Deep Learning model, providing state-of-the-art accuracy to many emerging classification problems. However, ConvNet classification is a computationally heavy task, suffering from rapid complexity scaling. This paper presents fpgaConvNet, a novel domain-specific modelling framework together with an automated design methodology for the mapping of ConvNets onto reconfigurable FPGA-based platforms. By interpreting ConvNet classification as a streaming application, the proposed framework employs the Synchronous Dataflow (SDF) model of computation as its basis and proposes a set of transformations on the SDF graph that explore the performance-resource design space, while taking into account platform-specific resource constraints. A comparison with existing ConvNet FPGA works shows that the proposed fully-automated methodology yields hardware designs that improve the performance density by up to 1.62× and reach up to 90.75% of the raw performance of architectures that are hand-tuned for particular ConvNets.
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Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs
Wei, X., Yu, C. H., Zhang, P., Chen, Y., Wang, Y., Hu, H., Liang, Y., & Cong, J. (2017) · Proceedings of the 54th Annual Design Automation Conference 2017 · doi:10.1145/3061639.3062207 · Cited by 426
fpgaConvNet: Mapping regular and irregular convolutional neural networks on FPGAs
Venieris, S. I., & Bouganis, C.-S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2844093 · Cited by 161
Self-citationA Neural Network-Based On-device Learning Anomaly Detector for Edge Devices
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Metadata
- Authors
- Venieris, S. I., & Bouganis, C.-S.
- Venue
- 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 40-47
- OpenAlex
- W2513568085
- Cited by
- 267
- References
- 16
- FWCI
- 21.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Venieris, S. I., & Bouganis, C.-S. (2016). fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs. 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). https://doi.org/10.1109/fccm.2016.22
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