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SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization.

, , , , , , , and . CVPR, page 11216-11225. Computer Vision Foundation / IEEE, (2019)

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Tux2: Distributed Graph Computation for Machine Learning., , , , , , , and . NSDI, page 669-682. USENIX Association, (2017)An empirical study on program failures of deep learning jobs., , , , , and . ICSE, page 1159-1170. ACM, (2020)EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs., , , , , , , , , and 2 other author(s). SC, page 55:1-55:14. ACM, (2023)Crux: GPU-Efficient Communication Scheduling for Deep Learning Training., , , , , , , , and . SIGCOMM, page 1-15. ACM, (2024)Multi-residual unit fusion and Wasserstein distance-based deep transfer learning for mill load recognition., , and . Signal Image Video Process., 18 (4): 3187-3196 (June 2024)BeamRaster: A Practical Fast Massive MU-MIMO System With Pre-Computed Precoders., , , , , , and . IEEE Trans. Mob. Comput., 18 (5): 1014-1027 (2019)Balanced Sparsity for Efficient DNN Inference on GPU., , , , and . AAAI, page 5676-5683. AAAI Press, (2019)MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters., , , , , , , , , and . NSDI, page 945-960. USENIX Association, (2022)Focusing More on Conflicts with Mis-Predictions Helps Language Pre-Training., , , and . CoRR, (2020)Llumnix: Dynamic Scheduling for Large Language Model Serving., , , , , , and . OSDI, page 173-191. USENIX Association, (2024)