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Energy-efficient neural networks using approximate computation reuse.

X. Jiao, V. Akhlaghi, Y. Jiang, and R. Gupta. DATE, page 1223-1228. IEEE, (2018)

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Chuhan Jiao University of Stuttgart

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Energy-efficient neural networks using approximate computation reuse.X. Jiao, V. Akhlaghi, Y. Jiang, and R. Gupta. DATE, page 1223-1228. IEEE, (2018)Brain-Inspired Hyperdimensional Computing for Ultra-Efficient Edge AIH. Amrouch, M. Imani, X. Jiao, Y. Aloimonos, C. Fermuller, D. Yuan, D. Ma, H. Barkam, P. Genßler, and P. Sutor. 2022 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), page 25-34. Piscataway, IEEE, (2022)TCAD EIC Message: February 2019.P. Brisk, S. Chakraborty, C. Coelho, A. Gamatié, S. Ghosh, and X. Jiao. IEEE Trans. on CAD of Integrated Circuits and Systems, 38 (2): 197-198 (2019)An Effective Heuristic-Based Approach for Partitioning.X. Zhao, H. Zhang, Y. Jiang, S. Song, X. Jiao, and M. Gu. J. Applied Mathematics, (2013)Industry practice of coverage-guided enterprise Linux kernel fuzzing.H. Shi, R. Wang, Y. Fu, M. Wang, X. Shi, X. Jiao, H. Song, Y. Jiang, and J. Sun. ESEC/SIGSOFT FSE, page 986-995. ACM, (2019)Reliability Analysis of PLC Systems by Bayesian Network.H. Zhang, Y. Jiang, X. Jiao, X. Song, W. Hung, and M. Gu. SERE, page 283-290. IEEE, (2012)Supervised learning based model for predicting variability-induced timing errors.X. Jiao, A. Rahimi, B. Narayanaswamy, H. Fatemi, J. de Gyvez, and R. Gupta. NEWCAS, page 1-4. IEEE, (2015)Energy Efficient GPU Applications Through Computation Skip.D. Ma, and X. Jiao. ICESS, page 1-2. IEEE, (2019)SAFL: increasing and accelerating testing coverage with symbolic execution and guided fuzzing.M. Wang, J. Liang, Y. Chen, Y. Jiang, X. Jiao, H. Liu, X. Zhao, and J. Sun. ICSE (Companion Volume), page 61-64. ACM, (2018)Automated Architecture Search for Brain-inspired Hyperdimensional Computing.J. Yang, Y. Sheng, S. Zhang, R. Wang, K. Foreman, M. Paige, X. Jiao, W. Jiang, and L. Yang. CoRR, (2022)

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Disambiguation of "Jiao, Xun"

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Energy-efficient neural networks using approximate computation reuse.

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Chuhan Jiao University of Stuttgart

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Energy-efficient neural networks using approximate computation reuse.

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Chuhan Jiao University of Stuttgart

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Energy-efficient neural networks using approximate computation reuse.