The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks
Abstract
Convolutional Neural Networks (CNN) are very computation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this paper, we propose a flexible dataflow architecture (FlexFlow) that can leverage the complementary effects among feature map, neuron, and synapse parallelism to mitigate the mismatch. We evaluated our design with six typical practical workloads, it acquires 2-10x performance speedup and 2.5-10x power efficiency improvement compared with three state-of-the-art accelerator architectures. Meanwhile, FlexFlow is highly scalable with growing computing engine scale.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
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 Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3226481 · Cited by 436
“FlexFlow [311] is a scalable accelerator that exploits the parallelism of computing platforms and CNN architectures to support a flexible data flow and data reuse.”
BackgroundHeterogeneous Dataflow Accelerators for Multi-DNN Workloads
Kwon, H., Lai, L., Pellauer, M., Krishna, T., Chen, Y.-H., & Chandra, V. (2021) · 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca51647.2021.00016 · Cited by 144
HybridFlow: A Flexible and Efficient RLHF Framework
Sheng, G., Zhang, C., Ye, Z., Wu, X., Zhang, W., Zhang, R., Peng, Y., Lin, H., & Wu, C. (2025) · Proceedings of the Twentieth European Conference on Computer Systems · doi:10.1145/3689031.3696075 · Cited by 74
HybridFlow: A Flexible and Efficient RLHF Framework
Sheng, G., Zhang, C., Ye, Z., Wu, X., Zhang, W., Zhang, R., Peng, Y., Lin, H., & Wu, C. (2024) · arXiv · doi:10.48550/arxiv.2409.19256 · Cited by 74
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores
Kao, S.-C., & Krishna, T. (2022) · 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca53966.2022.00065 · Cited by 58
Metadata
- Authors
- Lu, W., Yan, G., Li, J., Gong, S., Han, Y., & Li, X.
- Venue
- 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)
- Publisher
- IEEE
- Published
- 2017-02
- Volume
- 2017
- Pages
- 553-564
- ISSN
- 1530-0897 (ISSN-L)
- OpenAlex
- W2612076670
- Cited by
- 333
- References
- 4
- FWCI
- 25.96
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks. https://app.cochat.ai/papers/10.1109/hpca.2017.29
- The paper
- Lu, W., Yan, G., Li, J., Gong, S., Han, Y., & Li, X. (2017). FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks. 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA). https://doi.org/10.1109/hpca.2017.29
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.
