EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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Power Stabilization for AI Training Data centers
10 Pith papers cite this work. Polarity classification is still indexing.
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AI data centers raise surrounding land surface temperatures by 2°C on average, potentially affecting over 340 million people via local climate changes.
Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
AI data centers disrupt grid load diversity with rapid hundreds-of-MW swings, requiring explicit co-design between compute and power systems instead of implicit coexistence.
The paper proposes a dual PQ approach for voltage ride-through in large loads, showing that traditional reactive power compensation is limited by infrastructure constraints and that extreme dips may force disconnection.
Workload composition in AI data centers decouples aggregate power variability from short-horizon ramping through asymmetric queueing where batch jobs fill inference-induced idle capacity.
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
A representative feedback system and EMT simulations are used to analyze and reproduce the real-world 23-Hz oscillations from a large electronic load, with results matching fault recorder data.
A hierarchical review of energy storage technologies for smoothing the sub-second variable loads of AI data centers on the utility grid.
AI sovereignty requires coordinated design of data centers, optical networks, and real-time control systems to operate within energy availability and sustainability constraints.
citing papers explorer
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EasyRider: Mitigating Power Transients in Datacenter-Scale Training Workloads
EasyRider uses passive components plus actively controlled energy storage at the rack level, paired with lifetime-maximizing software, to keep AI training power transients inside grid safety limits without code changes or energy waste.
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The data heat island effect: quantifying the impact of AI data centers in a warming world
AI data centers raise surrounding land surface temperatures by 2°C on average, potentially affecting over 340 million people via local climate changes.
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Benchmarking Compound AI Applications for Hardware-Software Co-Design
Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
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From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
AI data centers disrupt grid load diversity with rapid hundreds-of-MW swings, requiring explicit co-design between compute and power systems instead of implicit coexistence.
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Voltage Ride-Through in Large Loads- A Dual PQ Approach
The paper proposes a dual PQ approach for voltage ride-through in large loads, showing that traditional reactive power compensation is limited by infrastructure constraints and that extreme dips may force disconnection.
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Workload composition smooths aggregate power demand while sustaining short-horizon ramps in AI data centers
Workload composition in AI data centers decouples aggregate power variability from short-horizon ramping through asymmetric queueing where batch jobs fill inference-induced idle capacity.
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Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
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Replicating Real-World 23-Hz Oscillations Caused by Large Electronic Loads
A representative feedback system and EMT simulations are used to analyze and reproduce the real-world 23-Hz oscillations from a large electronic load, with results matching fault recorder data.
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Grid Integration of AI Data Centers: A Critical Review of Energy Storage Solutions
A hierarchical review of energy storage technologies for smoothing the sub-second variable loads of AI data centers on the utility grid.
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AI Infrastructure Sovereignty
AI sovereignty requires coordinated design of data centers, optical networks, and real-time control systems to operate within energy availability and sustainability constraints.