Abstract
The Spiking Neural Network (SNN) is currently considered as a next generation neural network model. However, its performance often lags that of classical Artificial Neural Networks. Although there has been a wide range of research to improve the accuracy of SNNs, their performance is determined not only by accuracy, but also by speed and energy efficiency. In this study, we analyzed the relationship between hyperparameters, accuracy, speed and energy of SNN, set a new criterion to estimate the comprehensive performance and applied the Neuro-evolutionary algorithm to balance the hyperparameters without the need for manually setting them. The optimized model showed better performance in all terms of our criteria.
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References
Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997)
Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, C., Nakamura, Y., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)
Rueckauer, B., Hu, Y., Lungu, I.-A., Pfeiffer, M., Liu, S.-C.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017)
Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 508 (2016)
Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based spiking deep neural networks for object recognition. arXiv preprint arXiv:1611.01421 (2016)
Wiklendt, L., Chalup, S.K., Seron, M.M.: Simulated 3D biped walking with an evolution-strategy tuned spiking neural network. Neural Netw. World 19(2), 235 (2009)
Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.-C., Pfeiffer, M.: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: 2015 International Joint Conference on Neural Networks, pp. 1–8 (2015)
Diehl, P.U., Zarrella, G., Cassidy, A., Pedroni, B.U., Neftci, E.: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In: IEEE International Conference on Rebooting Computing, pp. 1–8 (2016)
LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database. AT&T Labs, vol. 2 (2010). http://yann.lecun.com/exdb/mnist
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Le, Q., Kurakin, A.: Large-scale evolution of image classifiers. arXiv preprint arXiv:1703.01041 (2017)
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)
Robertson, S.: PyTorch Tutorial (2017). http://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
Acknowledgments
This work was supported by the KIST Institutional Program (Project No. 2E27330-17-P026), and by the ICT R&D program of MSIP/IITP. [R-20161130-004520, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion]
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Kim, J., Kim, DS. (2018). Competitive Hyperparameter Balancing on Spiking Neural Network for a Fast, Accurate and Energy-Efficient Inference. 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_6
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DOI: https://doi.org/10.1007/978-3-319-92537-0_6
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