16 citations · 17 across the 7 of their papers we have counts for
Showing 2025 · cs.LGShow all
3 papers · 2 filters
cs.LG2025
SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
Wentao Guo, Mayank Mishra, Xinle Cheng +2
Mixture of Experts (MoE) models have emerged as the de facto architecture for scaling up language models without significantly increasing the computational cost. Recent MoE models…
cs.LG2025
Log-Linear Attention
Han Guo, Songlin Yang, Tarushii Goel +3
The attention mechanism in Transformers is an important primitive for accurate and scalable sequence modeling. Its quadratic-compute and linear-memory complexity however remain sig…
cs.LG2025
Hardware-Efficient Attention for Fast Decoding
Ted Zadouri, Hubert Strauss, Tri Dao
LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequent…