Abstract
In this paper, a pointer network model for traveling salesman problem (TSP) was implemented on a Xilinx PYNQ board which supports Python and Jupyter notebook and is equipped with ZYNQ SOC. We implement a pointer network model for solving TSP with Python and Theano firstly, then train the model on a GPU platform, and eventually deploy the model on a PYNQ board. Unlike traditional neural network implementation, hardware libraries on PYNQ (Overlays) are used to accelerate the pointer network model application. The experimental results show that the pointer network model for TSP can be deployed on the embedded system successfully and achieve good performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). Nature Publishing Group
Li, S., Wu, C., Li, H., Li, B., Wang, Y., Qiu, Q.: FPGA acceleration of recurrent neural network based language model. In: Proceedings of the IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 111–118. IEEE Press (2015)
Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015). SAGE Publications Sage UK, London
Hao, Y., Quigley, S.: The implementation of a deep recurrent neural network language model on a Xilinx FPGA. arXiv preprint arXiv:1710.10296 (2017)
Umuroglu, Y., Fraser, N.J., Gambardella, G., Blott, M., Leong, P., Jahre, M., Vissers, K.: Finn: a framework for fast, scalable binarized neural network inference. In: Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 65–74 (2017)
Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)
Guan, Y., Yuan, Z., Sun, G., Cong, J.: FPGA-based accelerator for long short-term memory recurrent neural networks. In: Proceedings of the 22nd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 629–634. IEEE Press (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)
Chang, A.X.M., Martini, B., Culurciello, E.: Recurrent neural networks hardware implementation on FPGA. arXiv preprint arXiv:1511.05552 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Milan, A., Rezatfighi, S.H., Garg, R., Dick, A.R., Reid, I.D.: Data-driven approximations to NP-hard problems. In: Proceedings of the 31st AAAI Conference on Artficial Intelligence (AAAI 2017), pp. 1453–1459 (2017)
Acknowledgments
The work described in the paper was supported by the National Science Foundation of China under Grant 61503233.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Gu, S., Hao, T., Yang, S. (2018). The Implementation of a Pointer Network Model for Traveling Salesman Problem on a Xilinx PYNQ Board. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-92537-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92536-3
Online ISBN: 978-3-319-92537-0
eBook Packages: Computer ScienceComputer Science (R0)