17 citations · 22 across the 8 of their papers we have counts for
8 papers
Potential Applications of HBF in LLM Serving Systems
Yihan Yin, Yinlun Zhao, Zhixin Yun +8
LLM serving is increasingly constrained by memory capacity as model weights, KV caches, and the number of served model variants continue to grow. This report examines High-Bandwidt…
LaMoSys3.5D: Enabling 3.5D-IC-Based Large Language Model Inference Serving Systems via Hardware/Software Co-Design
Qipan Wang, Zhe Zhang, Shuangchen Li +5
The success of large language models LLMs amplifies the need for highthroughput energyefficient inference at scale. 3DDRAMbased accelerators provide high memory bandwidth and there…
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
Yuanwei Fang, Zihao Liu, Yanheng Lu +7
With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling.…
Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio
Zhaoyang Du, Yijin Guan, Tianchan Guan +7
Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structur…
OpSparse: a Highly Optimized Framework for Sparse General Matrix Multiplication on GPUs
Zhaoyang Du, Yijin Guan, Tianchan Guan +4
Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM's inherent irregularity and the vas…
Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging
Zhaoyang Du, Yijin Guan, Tianchan Guan +3
Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast an…