液冷与智算中心日报｜2026-06-22
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，扩建继续推进，但电力、选址审批与能源获取仍是主约束，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact o…
The scalability and long-term reliability of 2.5D System-in-Package (SiP) platforms are increasingly governed by complex thermal ma…
展开全文
System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact of TCB vs HCB HBM Platforms
- 发布时间
- 2026-05-26
- 作者
- Woohyun Park、Youchang Na、S. Hong、Yoko Tomo、H. Yu、Yanggyoo Jung、Gyungbum Kim、H. Kang
- 主题
- 芯片与算力
摘要
The scalability and long-term reliability of 2.5D System-in-Package (SiP) platforms are increasingly governed by complex thermal management requirements, particularly as the integration of High-Bandwidth Memory (HBM) introduces concentrated heat profiles that challenge the system’s operational limits. The package platform—Thermo-Compression Bonding (TCB) versus Hybrid Copper Bonding (HCB) of HBM—strongly influences intra- and inter-package thermal behavior. This work implements 2.5D system-in-package (SiP) thermal test vehicles (TTVs) in an Open Compute Project (OCP)-standard GPU server with embedded sensors and controllable heaters across HBM stacks and GPU dies, faithfully mirroring functional heterogeneous package floorplans. Experimental results demonstrate thermal nonlinearity - strong platform- and cooling-dependent. At 1030 W per package, HCB reduces intra-package GPU to HBM thermal crosstalk versus TCB by 2.2% under air cooling and 9.8% under liquid cooling, while inter-package thermal crosstalk varies by up to 13.7% across cooling conditions. Comparative evaluation confirms that HCB measurably improves thermal conduction, reducing both intra- and inter-package thermal resistance. From a data-center perspective, the reduction in GPU to HBM crosstalk resistance enables up to 0.9°C higher allowable coolant inlet temperature in liquid cooling relative to the TCB baseline, which translates to approximately 3% cooling power reduction and PUE improvement from 1.26 to 1.24. For a 1000-rack AI cluster, this corresponds to roughly 31 GWh annual energy savings. Measured thermal trends further indicate that as AI infrastructure evolves toward inference-heavy, memory-focused workloads with increased HBM base-die power, HCB platforms will deliver progressively larger thermal benefits due to the shift toward more vertical-resistance-limited behavior. This study establishes GPU server-integrated 2.5D SiP TTV methodology as a robust platform for system-level thermal validation and demonstrates that HBM platform selection directly impacts data-center operational efficiency and future inference scalability.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用实验验证、原型测试或测量对比，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Woohyun Park, Youchang Na, S. Hong, 等. System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact of TCB vs HCB HBM Platforms[J/OL]. Electronic Components and Technology Conference. (2026-05-26)[2026-06-22]. https://www.semanticscholar.org/paper/2ca4f8beb1ea19fe6d038cdee022de662a80ecd6.
Contextual Robust Optimization for AI Data Center Scheduling with Statist…
The rapid growth of AI workloads is substantially increasing data center electricity demand and carbon emissions, motivating the de…
展开全文
Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees
- 发布时间
- 2026-06-16
- 作者
- Yijie Yang、Xi Weng、Yue Chen
- 主题
- 算电协同
摘要
The rapid growth of AI workloads is substantially increasing data center electricity demand and carbon emissions, motivating the development of carbon-aware scheduling methods. However, effective scheduling is challenging because renewable generation and AI workloads are subject to forecast errors, while training and inference workloads exhibit heterogeneity in computational characteristics. This paper proposes a contextual robust optimization framework for AI data center operation. The proposed model explicitly captures the heterogeneous computational characteristics of AI training and inference workloads. To deal with renewable generation and workload forecast errors, we develop loss-based uncertainty learning models that directly map contextual features to covariate-dependent uncertainty sets. The resulting contextual joint chance-constrained scheduling problem is reformulated into a tractable robust optimization problem, and a calibration algorithm is developed to provide finite-sample probabilistic feasibility guarantees for multiple joint chance constraints. Numerical experiments based on real-world AI workload traces and renewable generation data show that the proposed method reduces operating costs by an average of 5.57% compared to benchmark methods while maintaining reliable feasibility and strong computational scalability.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Yijie Yang, Xi Weng, Yue Chen. Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees[J/OL]. (2026-06-16)[2026-06-22]. http://arxiv.org/abs/2606.17466v1.
Energy-Aware Computing in the Year 2026
High-Performance Computing (HPC) has recently entered the Exascale era, and considerable efforts are being made to fully harness th…
展开全文
Energy-Aware Computing in the Year 2026
- 发布时间
- 2026-05-23
- 作者
- Roblex Nana Tchakoute、Claude Tadonki
- 主题
- AI 运维优化
摘要
High-Performance Computing (HPC) has recently entered the Exascale era, and considerable efforts are being made to fully harness this potential power for large-scale applications, such as cutting-edge generative AI (training and exploitation). The corresponding energy consumption is very high, and forecasts are alarming, making this metric a critical systemic bottleneck. Addressing this issue presents a genuine challenge for the entire cloud-edge-HPC continuum at all scales, from low-power IoT microcontrollers to multi-megawatt data centers. Beyond financial costs, green computing is driven by considerations related to climate change and environmental concerns such as carbon footprint ($CO_2e$), as well as constraints on energy production and supply, leading to a real need to regulate {\em information and communication technology} (ICT) activities. This article presents a comprehensive overview of energy-efficient computing, taking into account the most recent and significant contributions. Based on this exploration of the state of the art, we design and describe a holistic taxonomy of the aforementioned publications, structured around various perspectives, including {\em hardware and software aspects, measurement instrumentation, software optimizations, dynamic task scheduling, voltage scaling, workload consolidation, federated learning}, and {\em cooling}. Particular emphasis is placed on large-scale AI, which receives significant attention due to its considerable resource requirements. We conclude with an analysis of a forward-looking roadmap that considers the main perspectives of sustainable computing.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Roblex Nana Tchakoute, Claude Tadonki. Energy-Aware Computing in the Year 2026[J/OL]. (2026-05-23)[2026-06-22]. http://arxiv.org/abs/2605.24569v1.
AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects,…
The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, mem…
展开全文
AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects, Design Automation, and Embedded Intelligence
- 发布时间
- 2026-06-15
- 作者
- Mohamed M. Morsy
- 主题
- 芯片与算力
摘要
The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Mohamed M. Morsy. AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects, Design Automation, and Embedded Intelligence[J/OL]. Electronics. (2026-06-15)[2026-06-22]. https://www.semanticscholar.org/paper/6559f17a3e4aaa83cbf55ab2f8c0657056399288.
From Tokens to Energy Flexibility: Quantization-Enabled Demand Response f…
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-mana…
展开全文
From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads
- 发布时间
- 2026-06-17
- 作者
- Bojun Du、Xiaoyi Fan、Ershun Du、Long Chen、Jianpei Han、Qingchun Hou、Ning Zhang、Chongqing Kang
- 主题
- 算电协同
摘要
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data-center energy management mainly relies on temporal and spatial workload shifting and campus-level energy asset scheduling, but it usually treats LLM inference demand as an aggregate load. As a result, these approaches fail to exploit the internal characteristics of LLM serving and therefore overlook the flexibility offered by LLM-specific techniques such as model quantization. To unlock this flexibility, this paper proposes a quantization-enabled energy management framework for grid-responsive LLM inference data centers. First, a quantization-to-power model is established to map each model--quantization configuration to a compact set of dispatchable parameters. Second, a two-stage quantization-enabled DR model is developed to account for model instance switching, request routing, and precision selection. Third, a multi-campus co-optimization method is introduced for DR participation by integrating grid-side electricity and carbon signals with the quantization-enabled DR model. Case studies show that the proposed framework reduces total data-center operating cost by 34.3\% without curtailing served token volume, validating model quantization as an effective flexibility lever for grid-responsive LLM data-center energy management.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Bojun Du, Xiaoyi Fan, Ershun Du, 等. From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads[J/OL]. (2026-06-17)[2026-06-22]. http://arxiv.org/abs/2606.18851v1.
Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wa…
The rising demand for high-power semiconductor devices in sectors such as electric vehicles (EVs), renewable energy conversion, and…
展开全文
Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wafer Bonding and Embedded Microfluidic Cooling
- 发布时间
- 2026-05-26
- 作者
- Jiajing Nie、Jiuyang Tang、Hao Guan、Xinyue Wang、Tao Jiang、Junran Zhang、Guoqi Zhang、Guangyin Lei
- 主题
- 芯片与算力
摘要
The rising demand for high-power semiconductor devices in sectors such as electric vehicles (EVs), renewable energy conversion, and data centers highlights the need for efficient and reliable thermal management technologies. In this work, we present a simulation-based study of a 1200 V SiC MOSFET wafer-level power package that integrates chip–package co-design, room-temperature wafer bonding, and embedded microfluidic cooling. By utilizing a room-temperature bonding process to mitigate fabrication-induced warpage and optimizing the chip geometry to balance thermal spreading with mechanical stress, this proposed architecture ensures structural integrity while maximizing heat transfer efficiency. Thermal-fluid-mechanical multiphysics modeling results revealed that the proposed wafer-level microfluidic package achieved a 35.14% reduction in total thermal resistance compared with conventional SiC MOSFET power modules. The design demonstrates improvements in junction temperature uniformity and overall heat dissipation efficiency, which is promising for next-generation high-power density applications.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向算力硬件、边缘计算或模型部署对基础设施的牵引。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Jiajing Nie, Jiuyang Tang, Hao Guan, 等. Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wafer Bonding and Embedded Microfluidic Cooling[J/OL]. Electronic Components and Technology Conference. (2026-05-26)[2026-06-22]. https://www.semanticscholar.org/paper/11fa662b073d777b3f9125fd8ef8a3bb5cf601cc.
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for…
As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sust…
展开全文
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation
- 发布时间
- 2026-06-10
- 作者
- Jason Crop、Hayden Moore、Sudeep Pasricha
- 主题
- 算电协同
摘要
As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sustainable growth. The long-standing assumption that "cooler is better", i.e., lower CPU temperatures reduce power, does not fully hold for modern low-voltage CPUs, where inverse temperature dependence (ITD) drives higher supply voltages at lower temperatures. This creates a non-monotonic performance-per-watt curve where efficiency peaks at an intermediate thermal point. In this paper, for the first time, we empirically characterize ITD on production Intel Xeon CPUs and demonstrate that efficiency-optimal temperatures are CPU part-specific, and frequently higher than typical data center operating conditions. Measurements from commercial cloud data center platforms (Amazon, Equinix) reveal that approximately half of modern high-power CPUs operate about 10°C below their efficiency-optimal thermal point. By implementing ITD-aware thermal grouping of CPUs and inlet temperature adjustments, data center operators can optimize facility-level cooling and overall sustainability. Our case study shows that this approach can reduce total data center energy by 4-13% without sacrificing performance or reliability.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Jason Crop, Hayden Moore, Sudeep Pasricha. Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation[J/OL]. (2026-06-10)[2026-06-22]. http://arxiv.org/abs/2606.11163v1.
Heat transfer and flow characteristics of bionic Victoria Amazonica liqui…
芯片与算力方向论文；Semantic Scholar 未提供可展示摘要，建议打开原文核验方法和数据边界。
展开全文
Heat transfer and flow characteristics of bionic Victoria Amazonica liquid cooling plate for thermal management of chips in data centers
- 发布时间
- 2026-06-01
- 作者
- Feng Zhou、Wenlong Gu、Wenlong Li、G. Ma
- 主题
- 芯片与算力
摘要
Semantic Scholar 未提供可展示的原文摘要；请打开论文链接查看全文摘要。
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向算力硬件、边缘计算或模型部署对基础设施的牵引。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。摘要缺失，建议优先打开原文查看方法、数据和边界条件。
参考文献
Feng Zhou, Wenlong Gu, Wenlong Li, 等. Heat transfer and flow characteristics of bionic Victoria Amazonica liquid cooling plate for thermal management of chips in data centers[J/OL]. International Communications in Heat and Mass Transfer. (2026-06-01)[2026-06-22]. https://www.semanticscholar.org/paper/11f6857398316b362b30dcdbd0b233df7100bb1e.
BluSky AI Inc. (OTCID: BSAI)
Emerging Growth Conference · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
Competitive Online Peak-Demand Minimization using Energy Storage
Cambridge Energy and Environment Group · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
展开全文
Data Center Leaders on Building AI’s Infrastructure
Bloomberg Live · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
The environmental impact of AI | Isha Gollapudi | TEDxNormal
TEDx Talks · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
WeCan'22: Brainstorming Session with the Audience - Minghua, George, Davi…
Noman Bashir · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
展开全文
Data Democratization Panel | Priya Donti, Julia Stewart Lowndes, Nikki Tu…
WiDS Worldwide · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
展开全文
电力并网与能源约束
本期命中 14 条，热度分 33。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
智算中心 CapEx/扩建
本期命中 8 条，热度分 17。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
AI 芯片供给与交付
本期命中 5 条，热度分 12。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：Sponsored: BESS Is becoming th…
发布时间：2026-06-20；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道（原文标题：Plans filed for three-buil…
发布时间：2026-06-20；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Building the data center workfo…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：DMG signs first prefab data cen…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
AI 算力基础设施动态：Data Center Dynamics 发布相关报道（原文标题：Amazon could sell Trainium A…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Hyperscale Data plans to deploy…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
AI 算力基础设施动态：Data Center Dynamics 发布相关报道，涉及 $5、10GW（原文标题：California startu…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；可核验指标：$5、10GW；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：AWS inks recycled water supply …
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
液冷与热管理进展：ServeTheHome 发布相关报道，涉及 2026 W（原文标题：81920 Cores Per Rack with AMD…
发布时间：2026-06-18；近 7 天补充观察，非 24 小时窗口内；可核验指标：2026 W；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
液冷与热管理进展：Data Center Knowledge 发布相关报道（原文标题：Evaporative Cooling in Data Ce…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
AI 算力基础设施动态：Data Center Knowledge 发布相关报道（原文标题：HPE, Vultr Go All In on AI …
发布时间：2026-06-17；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：From Grid Constraints to On-S…
发布时间：2026-06-17；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：AWS Announces Amazon EC2 G7 Instances Acc…
发布时间：2026-06-20；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Data Center Automation: What’…
发布时间：2026-06-17；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 $49（原文标题：Nuclear physics res…
发布时间：2026-06-20；近 7 天补充观察，非 24 小时窗口内；可核验指标：$49；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 $54（原文标题：Verse raises $54m in Se…
发布时间：2026-06-19；近 7 天补充观察，非 24 小时窗口内；可核验指标：$54；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
#HPCMatters - Asetek Liquid Cooling for HPC Data Centers
Asetek Inc · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
展开全文
ASML CEO on AI Demand, Data Centers in Space and Musk's Terafab
Bloomberg Television · 检索词：AI infrastructure datacenter panel discussion。用于补充产业、产品或工程部署观察。
展开全文
产业热度指数 10/10
产业热度指数为 10/10：本期自动化检索记录到 24 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
展开全文
NVIDIA Blackwell/GB200/GB300
今日延续上榜
展开全文
AI 芯片供给与交付
今日延续上榜
展开全文
智算中心 CapEx/扩建
今日延续上榜
展开全文
4. 最新视频观察
BluSky AI Inc. (OTCID: BSAI)
学术会议报告 · Emerging Growth Conference · 检索词：AI data center energy conference keynote
在 YouTube 打开Competitive Online Peak-Demand Minimization using Energy Storage
学术讲座 · Cambridge Energy and Environment Group · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开Data Center Leaders on Building AI’s Infrastructure
学术会议报告 · Bloomberg Live · 检索词：AI data center energy conference keynote
在 YouTube 打开The environmental impact of AI | Isha Gollapudi | TEDxNormal
学术会议报告 · TEDx Talks · 检索词：AI data center energy conference keynote
在 YouTube 打开WeCan'22: Brainstorming Session with the Audience - Minghua, George, David, and Jay
学术讲座 · Noman Bashir · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开Data Democratization Panel | Priya Donti, Julia Stewart Lowndes, Nikki Tulley, Michela Taufer
学术讲座 · WiDS Worldwide · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开#HPCMatters - Asetek Liquid Cooling for HPC Data Centers
技术研讨会 · Asetek Inc · 检索词：high performance computing data center cooling workshop
在 YouTube 打开ASML CEO on AI Demand, Data Centers in Space and Musk's Terafab
专家圆桌 · Bloomberg Television · 检索词：AI infrastructure datacenter panel discussion
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 公开 RSS/Atom：The Register：未检索到符合条件的高相关条目。
- 论文池：已从本地论文池读取 22 条候选；池更新时间 2026-06-22 08:14。
- x.ai 论文解读：文本生成失败，已回退到规则化论文摘要；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 8 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- AI 分析：x.ai 调用失败，已回退到规则化模板；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit…