3 papers
cs.AR2025
Designing Spatial Architectures for Sparse Attention: STAR Accelerator via Cross-Stage Tiling
Huizheng Wang, Taiquan Wei, Hongbin Wang +6
Large language models (LLMs) rely on self-attention for contextual understanding, demanding high-throughput inference and large-scale token parallelism (LTPP). Existing dynamic spa…
cs.DC2025
MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference
Xinru Tang, Jingxiang Hou, Dingcheng Jiang +9
As large language models (LLMs) continue to scale up, mixture-of-experts (MoE) has become a common technology in SOTA models. MoE models rely on expert parallelism (EP) to alleviat…
cs.AI2024
Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels
Mingcong Song, Xinru Tang, Fengfan Hou +9
Meeting growing demands for low latency and cost efficiency in production-grade large language model (LLM) serving systems requires integrating advanced optimization techniques. Ho…