1 citations · 1 across the 5 of their papers we have counts for
5 papers
TEMP: A Memory Efficient Physical-aware Tensor Partition-Mapping Framework on Wafer-scale Chips
Huizheng Wang, Taiquan Wei, Zichuan Wang +8
Large language models (LLMs) demand significant memory and computation resources. Wafer-scale chips (WSCs) provide high computation power and die-to-die (D2D) bandwidth but face a…
MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference
Xinru Tang, Jingxiang Hou, Dingcheng Jiang +9
As large language models (LLMs) continue to scale up, mixture-of-experts (MoE) has become a common technology in SOTA models. MoE models rely on expert parallelism (EP) to alleviat…
PALM: A Efficient Performance Simulator for Tiled Accelerators with Large-scale Model Training
Jiahao Fang, Huizheng Wang, Qize Yang +5
Deep learning (DL) models are piquing high interest and scaling at an unprecedented rate. To this end, a handful of tiled accelerators have been proposed to support such large-scal…
Efficient Orchestrated AI Workflows Execution on Scale-out Spatial Architecture
Jinyi Deng, Xinru Tang, Zhiheng Yue +8
Given the increasing complexity of AI applications, traditional spatial architectures frequently fall short. Our analysis identifies a pattern of interconnected, multi-faceted task…
Wafer-scale Computing: Advancements, Challenges, and Future Perspectives
Yang Hu, Xinhan Lin, Huizheng Wang +12
Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand f…