Abstract
Today, Neural Networks are the basis of breakthroughs in virtually every technical domain. Their application to accelerators has recently resulted in better performance and efficiency in these systems. At the same time, the increasing hardware failures due to the latest semiconductor technology need to be addressed. Since accelerator systems are often used to back time-critical applications such as self-driving cars or medical diagnosis applications, these hardware failures must be eliminated. Our research evaluates these failures from a systemic point of view. Based on our results, we find critical results for the system reliability enhancement, and we further put forth an efficient method to avoid these failures with minimal hardware overhead.
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
Gatys, L. A., A. S. Ecker, and M. Bethge. 2016. Image style transfer using convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Collobert, R., and J. Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, ser. ICML ’08. New York, NY, USA: ACM, pp. 160–167. [Online]. Available: https://doi.org/10.1145/1390156.1390177.
Chen, C., A. Seff, A. Kornhauser, and J. Xiao. 2015. Deepdriving: Learning affordance for direct perception in autonomous driving. In The IEEE International Conference on Computer Vision (ICCV).
Chen, T., Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam. 2014. Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning. In ACM Sigplan Notices, vol. 49, no. 4. ACM, pp. 269–284.
Chen, Y., T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun et al. 2014. Dadiannao: A machine-learning supercomputer. In Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, pp. 609–622.
Chen, Y.-H., J. Emer, and V. Sze. 2016. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks. In ACM SIGARCH Computer Architecture News, vol. 44, no. 3. IEEE Press, pp. 367–379.
Zhang, C., P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong. 2015. Optimizing fpga-based accelerator design for deep convolutional neural networks. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, ser. FPGA ’15. New York, NY, USA: ACM, pp. 161–170. [Online]. Available: https://doi.org/10.1145/2684746.2689060.
Han, S., X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally. 2016. Eie: Efficient inference engine on compressed deep neural network. In 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pp. 243–254.
Protzel, P.W., D.L. Palumbo, and M.K. Arras. 1993. Performance and fault tolerance of neural networks for optimization. IEEE Transactions on Neural Networks 4 (4): 600–614.
Reagen, B., U. Gupta, L. Pentecost, P. Whatmough, S. K. Lee, N. Mulholland, D. Brooks, and G.-Y. Wei. 2018. Ares: A Framework for Quantifying the Resilience of Deep Neural Networks, pp. 1–6.
Kausar, F., and P. Aishwarya. 2016. Artificial neural network: Framework for fault tolerance and future. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), March 2016, pp. 648–651.
Li, G., K. Pattabiraman, and N. Debardeleben. 2018. Tensorfi: A configurable fault injector for tensorflow applications. In 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
Li, G., S. K. S. Hari, M. Sullivan, T. Tsai, K. Pattabiraman, J. Emer, and S. W. Keckler. 2017. Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications, pp. 1–12.
SalamiB., , O. Unsal, and A. Cristal. 2018. On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation. [Online]. Available: http://arxiv.org/abs/1806.09679.
Li, S., J. Niu, and Z. Li. 2021. Novelty detection of cable-stayed bridges based on cable force correlation exploration using spatiotemporal graph convolutional networks. Structural Health Monitoring, p. 1475921720988666.
Shi, C., Y. Ren, H. Tang, and L. R. Mupfukirei. 2021. A fault diagnosis method for an electro-hydraulic directional valve based on intrinsic mode functions and weighted densely connected convolutional networks. Measurement Science and Technology, 32(8), 084015.
Xilinx Inc. 2018. Vivado design suite 7 series fpga and zynq-7000 soc libraries guide. http://www.xilinx.com/support/documentation/swmanuals/xilinx20174/ug953-vivado-7series-libraries.pdf, UG953(v2017.4).
Xilinx Inc. 2016. Axi hwicap v3.0 logicore ip product guide. http://www.xilinx.com/support/documentation/ipdocumentation/axihwicap/v3 0/pg134-axi-hwicap.pdf, PG134.
Xilinx Inc.. 2018. 7 series fpgas configuration user guide. http://www.xilinx.com/support/documentation/user guides/ug470 7Series Config.pdf, UG470 (v1.13.1).
Redmon, J., and A. Farhadi. 2016. Yolo9000: Better, Faster, Stronger. arXiv preprint arXiv:1612.08242.
He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Sak, H., A. Senior, and F. Beaufays. 2014. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Fifteenth Annual Conference of the International Speech Communication Association.
Radford, A., L. Metz, and S. Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434.
Ebrahimi, M., A. Mohammadi, A. Ejlali, and S.G. Miremadi. 2014. A fast, flexible, and easy-to-develop FPGA-based fault injection technique. Microelectronics Reliability 54 (5): 1000–1008.
Lopez-Ongil, C., L. Entrena, M. Garcia-Valderas, M. Portela, and F. Munoz. 2007. A unified environment for fault injection at any design level based on emulation. IEEE Transactions on Nuclear Science 54 (4): 946–950.
Harward, N. A., M. R. Gardiner, L. W. Hsiao, and M. J. Wirthlin. 2015. Estimating soft processor soft error sensitivity through fault injection. In IEEE International Symposium on Field-programmable Custom Computing Machines.
Tarrillo J., J. Tonfat, L. Tambara, F. L. Kastensmidt, and R. Reis. 2015. Multiple fault injection platform for sram-based fpga based on ground level radiation experiments. In Test Symposium (2015).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sraw, J.S., Deepak, M.C. (2022). Using Convolutional Neural Networks for Fault Analysis and Alleviation in Accelerator Systems. In: Peng, SL., Lin, CK., Pal, S. (eds) Proceedings of 2nd International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1422. Springer, Singapore. https://doi.org/10.1007/978-981-19-0182-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-19-0182-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0181-2
Online ISBN: 978-981-19-0182-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)