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Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training., , , , , , , , , and 5 other author(s). CoRR, (2022)Adaptive Read Thresholds for NAND Flash., , , , and . CoRR, (2022)Shfl-BW: accelerating deep neural network inference with tensor-core aware weight pruning., , , , , and . DAC, page 1153-1158. ACM, (2022)Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors., , , , , , and . ICLR, OpenReview.net, (2023)You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding., , , , , , , , , and 4 other author(s). ECCV (12), volume 13672 of Lecture Notes in Computer Science, page 34-51. Springer, (2022)ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training., , , , , , , , , and 2 other author(s). DATE, page 1-6. IEEE, (2023)Towards Real-Time Segmentation on the Edge., , , , , , , , , and 2 other author(s). AAAI, page 1468-1476. AAAI Press, (2023)Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training., , , , , , , , , and 5 other author(s). AAAI, page 8360-8368. AAAI Press, (2023)INVITED: Computation on Sparse Neural Networks and its Implications for Future Hardware., , , , , and . DAC, page 1-6. IEEE, (2020)Noisy Computations during Inference: Harmful or Helpful?, and . CoRR, (2018)