Fault tolerance in neuromorphic computing systems の履歴(No.7)

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Vu Huy The

please add well known reference realetd to FT in neuromorphic.

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Please complte this survey by August 20, 2019

• 2019: J. Liu et al., Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network, in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 865-875, March 2019. doi: 10.1109/TNNLS.2018.2854291

• 2019: Mengyun Liu et. al., Fault tolerance in neuromorphic computing systems, ASPDAC '19 Proceedings of the 24th Asia and South Pacific Design Automation Conference Pages 216-223, Tokyo, Japan — January 21 - 24, 2019

• 2019: Z. Ye, R. Liu et.al., Evaluation of Radiation Effects in RRAM-Based Neuromorphic Computing System for Inference, in IEEE Transactions on Nuclear Science, vol. 66, no. 1, pp. 97-103, Jan. 2019. doi: 10.1109/TNS.2018.2886793

• 2019 J. J. Zhang, K. Basu and S. Garg, Fault-tolerant Systolic Array Based Accelerators for Deep Neural Network Execution, in IEEE Design & Test. doi: 10.1109/MDAT.2019.2915656

• 2019: L. Xia et.al., Fault-Tolerant Training Enabled by On-Line Fault Detection for RRAM-Based Neural Computing Systems, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. doi: 10.1109/TCAD.2018.2855145

• 2019: C. Torres-Huitzil and B. Girau, Fault and Error Tolerance in Neural Networks: A Review, in IEEE Access, vol. 5, pp. 17322-17341, 2017. doi: 10.1109/ACCESS.2017.2742698

• 2018: J. Liu et.al., SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture, in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1287-1300, April 2018.

• 2017: L. Xia et al., Fault-tolerant training with on-line fault detection for RRAM-based neural computing systems, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC), Austin, TX, 2017, pp. 1-6. doi: 10.1145/3061639.3062248

• 2015: M. Naeem et.al., On the role of astroglial syncytia in self-repairing spiking neural networks, in IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2370-2380, Oct. 2015. doi: 10.1109/TNNLS.2014.2382334