Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-Based Vision
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62874-0_9
Reference12 articles.
1. Abi-Karam, S., He, Y., Sarkar, R., Sathidevi, L., Qiao, Z., Hao, C.: Gengnn: a generic fpga framework for graph neural network acceleration. arXiv preprint arXiv:2201.08475 (2022). https://doi.org/10.48550/arXiv.2201.08475
2. Bi, Y., Chadha, A., Abbas, A., , Bourtsoulatze, E., Andreopoulos, Y.: Graph-based Object Classification for Neuromorphic Vision Sensing. In: 2019 IEEE International Conference on Computer Vision (ICCV). IEEE (2019). https://doi.org/10.1109/ICCV.2019.00058
3. Cordone, L., Miramond, B., Thierion, P.: Object detection with spiking neural networks on automotive event data. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN) (July 2022). https://doi.org/10.1109/IJCNN55064.2022.9892618
4. Engelhardt, N., So, H.K.H.: GraVF-M: graph processing system generation for multi-FPGA platforms. ACM Trans. Reconfigurable Technol. Syst. (TRETS) 12(4), 1–28 (2019). https://doi.org/10.1145/3357596
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