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cs.LG2025
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
cs.LG2024★ 1 cited
Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
Peizhuang Cong, Aomufei Yuan, Shimao Chen +4
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating…