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Yiting Luo

2 accepted papers

2023

Bi-directional Masks for Efficient N:M Sparse Training

ICML 2023poster

We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves practical speedups supported by the N:M sparse tensor core. Therefore, we present a novel method of Bi-directional Masks…

2022

Learning Best Combination for Efficient N:M Sparsity

NeurIPS 2022accept

By forcing N out of M consecutive weights to be non-zero, the recent N:M fine-grained network sparsity has received increasing attention with its two attractive advantages over traditional irregular network sparsity methods: 1) Promising performance at a high sparsity. 2) Significant speedups when p…