WSC-LLM: Efficient LLM Service and Architecture Co-exploration for Wafer-scale Chips
Zheng Xu, Dehao Kong, Jiaxin Liu, Jinxi Li, Jingxiang Hou, Xu Dai, Chao Li, Shaojun Wei, Yang Hu, Shouyi Yin
Abstract
The deployment of large language models (LLMs) imposes significant demands on computing, memory, and communication resources.Wafer-scale technology enables the high-density integration of multiple single-die chips with high-speed Die-to-Die (D2D) interconnections, presenting a promising solution to meet these demands arising from LLMs.However, given the limited wafer area, a trade-off needs to be made among computing, storage, and communication resources.Maximizing the benefits and minimizing the drawbacks of wafer-scale technology is crucial for enhancing the performance of LLM service systems, which poses challenges to both architecture and scheduling.Unfortunately, existing methods cannot effectively address these challenges.To bridge the gap, we propose WSC-LLM, an architecture and scheduling co-exploration framework.We first define a highly configurable general hardware template designed to explore optimal architectural parameters for wafer-scale chips.Based on it, we
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Cited by top-tier papers4
- TEMP: A Memory Efficient Physical-Aware Tensor Partition-Mapping Framework on Wafer-Scale ChipsHuizheng Wang, Taiquan Wei, Zichuan Wang, Dingcheng Jiang et al.HPCA 2026 · 2 citations
- WATOS: Efficient LLM Training Strategies and Architecture Co-Exploration for Wafer-Scale ChipHuizheng Wang, Zichuan Wang, Hongbin Wang, Jingxiang Hou et al.HPCA 2026 · 2 citations
- Ouroboros: Wafer-Scale SRAM CIM with Token-Grained Pipelining for Large Language Model InferenceYiqi Liu, Yudong Pan, Mengdi Wang, Shixin Zhao et al.ASPLOS 2026 · 1 citation
- Patterns Behind Chaos: Forecasting Data Movement for Efficient Large-Scale Moe LLM InferenceZhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou et al.ISCA 2026
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