7 papers
AgentKV: Phase-Aware KV Eviction for Agentic LLMs
Taowen Tony Liu, Jeffrey T. H. Wong, Can Xiao +3
Agentic serving can consume orders of magnitude more tokens than chatbot workloads, stressing both KV-cache capacity and decode-time bandwidth. Most KV-eviction methods score cache…
OasisKV: Scaling In-Decode KV Cache Beyond HBM with Lookahead Sparse Prefetching
Can Xiao, Sukmin Cho, Junbong We +7
Large language model (LLM) inference serving is increasingly constrained by memory rather than compute. As long-context and long-form reasoning workloads become more prevalent, the…
When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference
Przemyslaw Forys, Haoran Wu, Can Xiao +8
Agentic inference now dominates the LLM inference landscape, requiring LLMs to actively engage in multi-turn interactions with tool-calling capabilities. This introduces a more com…
MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs
Haoran Wu, Zeyu Cao, Yao Lai +16
Emerging agentic large language model (LLM) workloads are driving rapidly growing demand for memory capacity and bandwidth. Different phases of inference, such as prefill and decod…
NPU Design for Diffusion Language Model Inference
Binglei Lou, Haoran Wu, Kevin Lau +9
Diffusion-based LLMs (dLLMs) fundamentally depart from traditional autoregressive (AR) LLM inference: they leverage bidirectional attention, block-wise KV cache refreshing, cross-s…
Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
Haoran Wu, Can Xiao, Jiayi Nie +15
LLMs now form the backbone of AI agents across a diverse range of applications, including tool use, command-line interfaces, and web or computer interaction. These agentic LLM infe…
Refining Datapath for Microscaling ViTs
Can Xiao, Jianyi Cheng, Aaron Zhao
Vision Transformers (ViTs) leverage the transformer architecture to effectively capture global context, demonstrating strong performance in computer vision tasks. A major challenge…