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DC25_PAPER_Track1_ANovelThermoFlowUniformizerApplied_Son_V2.pdf

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1、 Information Classification:General A Novel ThermoFlow Uniformizer Applied Immersion Cooling System(TFU-ICS)for Highly-Dense Multi-GPU-HBM Based Compute Module for AI Supercomputer Keeyoung Son,KAIST keeyoungkaist.ac.kr,+82-10-8506-7299 Taein Shin,KAIST Haeyeon Rachel Kim,KAIST Hyunjun An,KAIST Joun

2、gho Kim,KAIST Information Classification:General Abstract AI supercomputers need more GPUs and higher bandwidth for high AI computing performance,however it increasing power consumption causes thermal issues that degrade GPU performance and affect signal integrity(SI),reducing bandwidth.To meet the

3、demands of advanced AI models,higher bandwidth and computing density are crucial.Immersion cooling system(ICS)with high cooling capacities are emerging as thermal solutions,but future AI supercomputers need even better cooling solutions.With stable thermal reliability,SI performance can improve,allo

4、wing closer GPU integration,higher computing density,and lower power consumption.Therefore,we proposed novel thermoflow uniformizer applied ICS(TFU-ICS)adapated multi-GPU-HBM based compute module for future AI supercomputer.The proposed TFU is novel heatsink that control flow regime of immersion coo

5、lant for enhancing ICS performance and causing uniform thermal distribution of compute module.TFU is designed to adjust immersion coolant flow rate by varying its thickness,considering power profile of compute module,resulting in stronger and uniform performance.Based on powerful cooling performance

6、 of TFU-ICS,we designed much compact and highly-dense multi-GPU-HBM,with 4 GPUs integrated on interposer,not board.We also designed interconnection between each GPUs on same interposer and GPUs on other compute module.This compute module provides higher bandwidth with low power than conventional com

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1. **提出TFU-ICS技术**:针对AI超算高密度多GPU-HBM计算模块的热管理挑战,提出热流均匀化浸没冷却系统(TFU-ICS),通过调节冷却剂流速实现均匀散热,解决传统L2A冷却不足问题(如GPT-5目标超算功率达100kW/机柜,冷却能力不足)。 2. **高计算密度设计**:TFU-ICS支持4个GPU集成于硅中介层,计算密度提升13倍,模块尺寸缩小50%,且无需传统D2C/TEC冷却器。 3. **热与信号完整性优化**:TFU根据功耗分布调整厚度(如高功耗区增厚),使GPU/HBM温度降至83°C/85°C以下(较无TFU时120°C显著降低),并减少温度波动,保障信号完整性(如HBM 2K I/O接口在8层RDL设计下满足10Gbps眼图要求)。 4. **性能优势**:TFU-ICS实现更高带宽(缩短互连长度)、更低功耗(PUE降低),且验证其热管理及SI性能优于传统L2A冷却,适用于超大规模AI模型训练。
**TFU-ICS如何提升AI算力?** **浸没冷却为何是未来趋势?** **高密度GPU如何散热?**
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