用于缓冲AI训练引起的数据中心级功率波动的有源滤波器设计
Active Filter Design for Buffering Datacenter-Scale Power Fluctuations from Training AI
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中文总结 AI 辅助
本文提出一种自动检测负载功率变化并用机架内储能补偿的电力传输架构，以缓冲AI训练引起的功率波动，实验验证可将电网侧功率上升速率限制在预设范围内，且无需更改系统软件。
中文摘要 AI 辅助
训练大型AI模型需要数千个处理器并行工作。这种同步负载对传统电力传输架构构成了挑战，因为IT功率的上升速度远快于电网硬件所能补偿的速度。本文提出了一种针对该问题的硬件解决方案，介绍了一种新颖的电力传输架构，该架构能够自动检测负载功率的变化，并利用机架内储能进行补偿，从而为电网响应变化负载提供时间。该架构通过实验验证，在供电训练负载的同时，将电网侧功率上升速率限制在预设范围内。所展示的原型额定可在400 VDC系统中提供高达10 kW的缓冲功率，物料清单成本为3500美元。本文详细描述了系统设计。该架构相较于其他方法的一个关键优势在于，它无需对系统软件进行任何更改即可可靠地缓冲功率波动，从而与任何训练工作负载兼容。
英文摘要
Training large AI models requires thousands of processors working in parallel. This synchronized load poses a challenge for traditional power delivery architectures, because IT power ramps much faster than grid hardware can compensate. This paper presents a hardware solution to this problem, introducing a novel power delivery architecture which automatically detects changes in load power and compensates using on-rack energy storage, giving time for the grid to respond to the varying load. The architecture is validated experimentally, powering a training load while limiting the grid-side power ramp rate such that it stays within pre-specified range. The prototype presented is rated to deliver up to 10 kW of buffered power in a 400 VDC system, with a bill of materials cost of $3,500 USD. The system design is described in detail. A key advantage of this architecture versus other approaches is that it reliably buffers power fluctuations without requiring any changes to system software, making it compatible with any training workload.
发表机构
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
