Research Article算电协同
Yijie Yang、Xiaochong Weng、Yue Chen
Published 2026-06-16 · Semantic Scholar · Credibility S
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 co…
Abstract, interpretation and reference
Abstract
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, Xiaochong Weng, Yue Chen. Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees[J/OL]. (2026-06-16)[2026-07-02]. https://www.semanticscholar.org/paper/4002a655c0c91986009f3172ac3568644528ceea.
Research Article芯片与算力
Sidharth Rajeev、Ketan Yogi、Venkata Achyuth Kunchapu、Yunchun Yang、Harish Kumar Lattupalli、Scott N. Schiffres、Tiwei Wei、S. Rangarajan
Published 2026-06-26 · Semantic Scholar · Credibility S
The rapid growth of digitalization and data-driven technologies is driving large-scale deployment of data centers worldwide. As societies become increasingly data-hungry, the energy consumption of data centers continues to rise at an unprecedented rate, with a significant fraction of this energy being expended on thermal management and cooling infrastructure. Improving cooling efficiency has therefore become a criti…
Abstract, interpretation and reference
Abstract
The rapid growth of digitalization and data-driven technologies is driving large-scale deployment of data centers worldwide. As societies become increasingly data-hungry, the energy consumption of data centers continues to rise at an unprecedented rate, with a significant fraction of this energy being expended on thermal management and cooling infrastructure. Improving cooling efficiency has therefore become a critical challenge for the thermal management community, directly impacting both the sustainability and scala-bility of future computing systems. In this manuscript, we demonstrate an energy-efficient cooling solution based on chip-integrated two-phase cooling, which leverages liquid to vapor phase-change heat transfer to achieve high heat-flux dissipation at reduced pumping power and thermal resistance. This paper investigates an aggressive cooling architecture utilizing direct-on-die two-phase jet impingement on an NVIDIA Tesla V100 GPU. By eliminating the TIM and impinging the working fluid?R1233zd(E), a low-GWP (< 1) refrigerant?directly onto the silicon backside, the primary thermal bottleneck is removed. Experimental results demonstrate a remarkably low thermal resistance of 0.056 °C/W and a theoretical pumping power of only 0.172 W. The system exhibits superior ther- mal stability and rapid transient response compared to conventional air-cooled solutions. Furthermore, the reliability of the direct-exposure manifold was validated through 200 hours of continuous, stable operation. This study positions direct jet impingement as a highly efficient, compact, and sustainable solution for next-generation high-performance computing (HPC) environments.
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
Sidharth Rajeev, Ketan Yogi, Venkata Achyuth Kunchapu, 等. Server-Level Demonstration of Package-Integrated, Two-Phase Jet Impingement Cooling on AI Chipsets[J/OL]. Journal of Electronic Packaging. (2026-06-26)[2026-07-02]. https://www.semanticscholar.org/paper/02f61b7cdb675d1bdef557d6de236a502f2d830b.
Research Article热管理与液冷
N. Nandakumar、Research Scholar、N. Mahibanlindsay、Professor Head、C. Professor
Published 2026-06-03 · Semantic Scholar · Credibility S
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。
Abstract, interpretation and reference
Abstract
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，液冷、热管理和数据中心能效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：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。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
N. Nandakumar, Research Scholar, N. Mahibanlindsay, 等. Power Optimization in Data Centres using Artificial Intelligence[J/OL]. 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA). (2026-06-03)[2026-07-02]. https://www.semanticscholar.org/paper/5b33a59bd51dfb893024c739ab73e404f2d42f5f.
Research Article算电协同
Yugui Liu、Yibo Ding、Xudong Li、Jing Qu、Wenyi Zhang、T. Qian、Wuyou Xiao、Zhengyang Hu
Published 2026-06-03 · Semantic Scholar · Credibility S
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critical need for computation-electricity coordination. Harnessing the spatio-temporal flexibility of DC workloads is a promising approach to facilitate this coordination. However, existing studies overlook the collaborative potential of computational resource sharing among geo-distributed DCs…
Abstract, interpretation and reference
Abstract
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critical need for computation-electricity coordination. Harnessing the spatio-temporal flexibility of DC workloads is a promising approach to facilitate this coordination. However, existing studies overlook the collaborative potential of computational resource sharing among geo-distributed DCs, thereby failing to fully unlock this flexibility. In this paper, a bi-level computation-electricity coordination framework is proposed to explicitly capture the bidirectional interactions between DCs and power grid. Firstly, a peer-to-peer cloud service market (P2P-CSM) for geo-distributed DCs is proposed, which enables bilateral cloud service transactions to leverage regional heterogeneities (e.g., electricity prices, cooling efficiency). Secondly, locational marginal prices are embedded into the framework to reflect network congestion and nodal price disparities. Thirdly, a dual consensus alternating direction method of multipliers (ADMM)-based decentralized algorithm is developed as the P2P market clearing algorithm, and a bisection-assisted iterative algorithm is proposed to ensure rigorous convergence of the framework. Case studies conducted on modified IEEE 30-bus system validate that the P2P-CSM achieves a win-win computation-electricity coordination: it not only increases total DC operational profit by 22.8\%, but also effectively alleviates grid congestion and yields a 3.2\% reduction in total energy consumption.
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：摘要显示作者采用框架构建和频域/系统级分析，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
Yugui Liu, Yibo Ding, Xudong Li, 等. Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination[J/OL]. (2026-06-03)[2026-07-02]. https://www.semanticscholar.org/paper/9962e96ebd978879cc56a88a44a99bc7fe6c5653.
Research Article芯片与算力
Mohamed M. Morsy
Published 2026-06-15 · Semantic Scholar · Credibility S
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 i…
Abstract, interpretation and reference
Abstract
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)[2026-07-02]. https://www.semanticscholar.org/paper/6559f17a3e4aaa83cbf55ab2f8c0657056399288.
Research Article热管理与液冷
Sangwhee Lee、Rafal P. Wojda、Cheol-Hee Jo、Shuntaro Inoue、Pedro Ribeiro、Gui-Jia Su、Mostak Mohammad、Himel Barua
Published 2026-06-23 · Semantic Scholar · Credibility S
The rapid growth of AI workloads is driving unprecedented increases in data center power demand, current transients, and thermal stress, exposing fundamental limitations in traditional 48 V rack architectures, low-voltage AC distribution, and line-frequency transformer interfaces. This paper reviews the three stages of architectural shifts required to support next-generation AI data centers and identifies three enab…
Abstract, interpretation and reference
Abstract
The rapid growth of AI workloads is driving unprecedented increases in data center power demand, current transients, and thermal stress, exposing fundamental limitations in traditional 48 V rack architectures, low-voltage AC distribution, and line-frequency transformer interfaces. This paper reviews the three stages of architectural shifts required to support next-generation AI data centers and identifies three enabling technological building blocks: high-voltage conversion-ratio DC/DC converters, facility-level low-voltage DC distribution, and medium-voltage solid-state transformers. The advantages, technical challenges, and potential solutions associated with each building block are reviewed. Finally, future research directions and open challenges are discussed.
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，液冷、热管理和数据中心能效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，它可用于判断液冷方案、热管理路线和高密度部署节奏。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
Sangwhee Lee, Rafal P. Wojda, Cheol-Hee Jo, 等. Toward Next-Generation AI Data Centers: Power Delivery Architecture Shifts, Emerging Technologies, and Challenges[J/OL]. (2026-06-23)[2026-07-02]. https://www.semanticscholar.org/paper/f179b2632112d6f9413ff1aefd4faa3fe00130f4.
Research Article芯片与算力
Feng Zhou、Wenlong Gu、Wenlong Li、G. Ma
Published 2026-06-01 · Semantic Scholar · Credibility S
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。
Abstract, interpretation and reference
Abstract
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)[2026-07-02]. https://www.semanticscholar.org/paper/11f6857398316b362b30dcdbd0b233df7100bb1e.