activity
20232026
most citedNeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics

Wonmi Choi, Minuk Park, Zhixiong Niu +3

LLM-based AI agents process user requests through iterative reasoning and tool execution, often involving the invocation of remote LLM APIs with local tool containers. This executi…

cs.AI2026

SKILL-DISCO: Distilling and Compiling Agent Traces into Reusable Procedural Skills

Zhongxin Guo, Danrui Qi, Hanwen Gu +2

Agents often repeatedly solve similar task instances from scratch, leading to unnecessary reasoning cost and long execution traces. Prior work has explored workflow reuse and execu…

cs.AR2026

LUMINA: LLM-Guided GPU Architecture Exploration via Bottleneck Analysis

Tao Zhang, Rui Ma, Shuotao Xu +2

GPU design space exploration (DSE) for modern AI workloads, such as Large-Language Model (LLM) inference, is challenging because of GPUs' vast, multi-modal design spaces, high simu…

cs.AR2025

TENET: An Efficient Sparsity-Aware LUT-Centric Architecture for Ternary LLM Inference On Edge

Zhirui Huang, Rui Ma, Shijie Cao +5

Ternary quantization has emerged as a powerful technique for reducing both computational and memory footprint of large language models (LLM), enabling efficient real-time inference…

cs.AR2024★ 2 cited

NeoMem: Hardware/Software Co-Design for CXL-Native Memory Tiering

Zhe Zhou, Yiqi Chen, Tao Zhang +8

The Compute Express Link (CXL) interconnect makes it feasible to integrate diverse types of memory into servers via its byte-addressable SerDes links. Considering the various acces…

cs.DC2024

SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation

Yifan Xiong, Yuting Jiang, Ziyue Yang +17

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently…