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Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks.

M. Rhu, M. O'Connor, N. Chatterjee, J. Pool, Y. Kwon, and S. Keckler. HPCA, page 78-91. IEEE Computer Society, (2018)

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Friedrich Rhumbler

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Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training.M. Zhu, J. Clemons, J. Pool, M. Rhu, S. Keckler, and Y. Xie. CoRR, (2018)Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training.Y. Kwon, Y. Lee, and M. Rhu. CoRR, (2020)FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems.H. Kim, Y. Lee, and M. Rhu. IEEE Comput. Archit. Lett., 23 (1): 7-10 (January 2024)Understanding the Implication of Non-Volatile Memory for Large-Scale Graph Neural Network Training.Y. Lee, Y. Kwon, and M. Rhu. IEEE Comput. Archit. Lett., 20 (2): 118-121 (2021)Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology.B. Hyun, T. Kim, D. Lee, and M. Rhu. CoRR, (2023)PREMA: A Predictive Multi-Task Scheduling Algorithm For Preemptible Neural Processing Units.Y. Choi, and M. Rhu. HPCA, page 220-233. IEEE, (2020)Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery.J. Ahn, C. Jin, J. Kim, M. Rhu, Y. Fei, D. Kaeli, and J. Kim. HPCA, page 332-344. IEEE, (2021)BTS: an accelerator for bootstrappable fully homomorphic encryption.S. Kim, J. Kim, M. Kim, W. Jung, J. Kim, M. Rhu, and J. Ahn. ISCA, page 711-725. ACM, (2022)Architecture design of a high-performance dual-symbol binary arithmetic coder for JPEG2000.M. Rhu, and I. Park. ICIP, page 2665-2668. IEEE, (2009)Beyond the Memory Wall: A Case for Memory-Centric HPC System for Deep Learning.Y. Kwon, and M. Rhu. MICRO, page 148-161. IEEE Computer Society, (2018)

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Disambiguation of "Rhu, Minsoo"

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Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks.

Please choose a person to relate this publication to

Friedrich Rhumbler

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Disambiguation of "Rhu, Minsoo"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks.

Please choose a person to relate this publication to

Friedrich Rhumbler

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Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks.