Papers by Ekaterina Galaeva
Motivating Next-Gen Accelerators with Flexible N:M Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches (2026.acl-industry)
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Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva, Alina Kostromina, Vladimir Smirnov, Redko Dmitry, Alexey Dontsov, Maxim Zhelnin, Evgeny Burnaev, Egor Shvetsov
| Challenge: | Recent studies show that sparsification is not supported in large language models. |
| Approach: | They propose to use activation pruning to accelerate large language models with sparsification . they compare activation pruners with weight pruner and activater pruning with activation . |
| Outcome: | The proposed approach outperforms weight pruning at matched sparsity levels. |