arXiv Machine Learning June 15, 2026 By Honghua Zhu, Chunjie Luo, Jianfeng Zhan

BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2606. 13747v1 Announce Type: cross Abstract: Accurate power estimation is important for understanding and optimizing CPU power behavior, yet practical workflows often rely on simulation-derived information or post-silicon analysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. However, direct measurement of inference energy often requires hardware telemetry, power instrumentation, or infrastructure-specific monitoring, limiting its applicability in comparative studies, early-stage system design, and sustainability reporting.

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