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Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing.

E. Ipek. IEEE Micro, 39 (1): 58-61 (2019)

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Self-Optimizing Memory Controllers: A Reinforcement Learning Approach.E. Ipek, O. Mutlu, J. Martínez, and R. Caruana. ISCA, page 39-50. IEEE Computer Society, (2008)AC-DIMM: associative computing with STT-MRAM.Q. Guo, X. Guo, R. Patel, E. Ipek, and E. Friedman. ISCA, page 189-200. ACM, (2013)Enabling Scientific Computing on Memristive Accelerators.B. Feinberg, U. Vengalam, N. Whitehair, S. Wang, and E. Ipek. ISCA, page 367-382. IEEE Computer Society, (2018)Enabling energy efficient Hybrid Memory Cube systems with erasure codes.S. Wang, Y. Song, M. Bojnordi, and E. Ipek. ISLPED, page 67-72. IEEE, (2015)Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing.E. Ipek. IEEE Micro, 39 (1): 58-61 (2019)Reducing data movement energy via online data clustering and encoding.S. Wang, and E. Ipek. MICRO, page 1-13. IEEE Computer Society, (2016)Vertical Writes: Closing the Throughput Gap between Deeply Scaled STT-MRAM and DRAM.E. Ipek, F. Longnos, S. Xiao, and W. Yang. IEEE Comput. Archit. Lett., 17 (2): 151-154 (2018)Energy-Efficient Nonvolatile Flip-Flop With Subnanosecond Data Backup Time for Fine-Grain Power Gating.M. Kazemi, E. Ipek, and E. Friedman. IEEE Trans. on Circuits and Systems, 62-II (12): 1154-1158 (2015)Phase-Change Technology and the Future of Main Memory.B. Lee, P. Zhou, J. Yang, Y. Zhang, B. Zhao, E. Ipek, O. Mutlu, and D. Burger. IEEE Micro, 30 (1): 143 (2010)Energy-efficient data movement with sparse transition encoding.Y. Song, M. Bojnordi, and E. Ipek. ICCD, page 399-402. IEEE Computer Society, (2015)

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Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing.

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Disambiguation of "Ipek, Engin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing.

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Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing.