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基于芯片片的异质集成人工智能芯片的智能热优化研究,嵌入了以叶脉为灵感的碎形微通道
Jie Wu1, Yu Liang1, Guibin Liu1
1College of Integrated Circuit Science and Engineering (College of Industry-Education Integration), Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Materials (Basel, Switzerland)
|February 27, 2026
概括
由于冷却不足,先进的AI芯片面临着热点. 一种新的叶脉折叠微通道设计显著降低了76%的AI芯片连接温度,提供了有效的热点缓解.
科学领域:
- 热管理 热管理
- 微流体学 微流体学
- 人工智能 硬件 硬件
背景情况:
- 具有异质集成的高性能AI芯片产生不均的热量.
- 传统的冷却与动态热图作斗争,导致热点.
- 基于芯片的设计加剧了热挑战.
研究的目的:
- 为人工智能处理器开发适应式冷却解决方案.
- 为了减轻异质AI包中的热点.
- 为了研究以叶子静脉为灵感的碎形微通道设计.
主要方法:
- 基于有限元法 (FEM) 的直角实验进行.
- 差异分析 (ANOVA) 和范围分析对排名参数的影响.
- 一个机器学习的替代模型与粒子集群优化 (PSO) 相结合被用于优化.
主要成果:
- 碎形微通道证明了超低的热阻和高的传热系数.
- 最佳参数选择显著降低了AI芯片连接温度.
- 冷却效率提高了76%,温度从127.80°C降至30.97°C.
结论:
- 以叶脉为灵感的碎形微通道有效地解决了AI芯片中的热点.
- 这种设计为先进的异质AI包提供了可扩展和高效的解决方案.
- 提供了下一代AI硬件热管理的理论基础.
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