3 papers
cs.LG2026
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
cs.LG2025
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
Tai An, Ruwu Cai, Yanzhe Zhang +6
In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focus…
cs.LG2025
Dynamic Low-Rank Sparse Adaptation for Large Language Models
Weizhong Huang, Yuxin Zhang, Xiawu Zheng +4
Despite the efficacy of network sparsity in alleviating the deployment strain of Large Language Models (LLMs), it endures significant performance degradation. Applying Low-Rank Ada…