2 papers
cs.LG2026
Small LLMs: Pruning vs. Training from Scratch
Yufeng Xu, Taiming Lu, Kunjun Li +3
Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5--0.8 with six methods spanning d…
cs.AR2026
Mitigating scalability challenges in LUT-based neural networks via pruning optimisations
Xuqi Zhu, Huaizhi Zhang, JunKyu Lee +5
Modern deep neural networks heavily rely on a large number of multiply-accumulate operations, which constitute the predominant computational cost. To address this, Look-Up Table (L…