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cs.LG2025
SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference
Wenxun Wang, Shuchang Zhou, Wenyu Sun +2
Transformers have shown remarkable performance in both natural language processing (NLP) and computer vision (CV) tasks. However, their real-time inference speed and efficiency are…
cs.LG2025
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
Zongle Huang, Lei Zhu, Zongyuan Zhan +5
Large Language Models (LLMs) have achieved remarkable success across many applications, with Mixture of Experts (MoE) models demonstrating great potential. Compared to traditional…
cs.DC2025
Enhancing Memory Efficiency in Large Language Model Training Through Chronos-aware Pipeline Parallelism
Xinyuan Lin, Chenlu Li, Zongle Huang +5
Larger model sizes and longer sequence lengths have empowered the Large Language Model (LLM) to achieve outstanding performance across various domains. However, this progress bring…