21 citations · 36 across the 13 of their papers we have counts for
7 papers · 1 filter
AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs
Cheuk Hang Lau, Zeyu Cao, Kevin Wong Cheuk Yin +6
The optimization of LLM serving engines, such as vLLM and SGLang, is largely benchmark-driven: optimizations, scheduling policies, hardware and system designs are all selected base…
LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching
Yao Lai, Xuyuan Xiong, Zeyue Xue +7
In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the wavelength of light, optical…
ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents
Fanqing Meng, Lingxiao Du, Zijian Wu +46
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…
AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval
Yihan Wang, Lei Li, Yao Lai +2
Analog circuit design relies heavily on reusing existing intellectual property (IP), yet searching across heterogeneous representations such as SPICE netlists, schematics, and func…
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…
KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware
Jiayi Nie, Haoran Wu, Yao Lai +9
New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels, a time-consuming and error-prone process that does…
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…