5 papers
Bit-Accurate Modeling of GPU Matrix Multiply-Accumulate Units: Demystifying Numerical Discrepancy and Accuracy
Peichen Xie, Shuotao Xu, Yang Wang +2
Modern AI accelerators rely on matrix multiply-accumulate units (MMAUs), such as NVIDIA Tensor Cores and AMD Matrix Cores, to accelerate deep neural network workloads. MMAUs expose…
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…
FengHuang: Next-Generation Memory Orchestration for AI Inferencing
Jiamin Li, Lei Qu, Tao Zhang +4
This document presents a vision for a novel AI infrastructure design that has been initially validated through inference simulations on state-of-the-art large language models. Adva…
SPFresh: Incremental In-Place Update for Billion-Scale Vector Search
Yuming Xu, Hengyu Liang, Jin Li +9
Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for sim…
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…