機械学習のためのゼロショット・マルチスコープLCAフレームワーク：炭素・水・ネットワークフットプリントの統合
A Zero-Shot Multi-Scope Life Cycle Assessment Framework for Machine Learning: Unifying Carbon, Water, and Network Footprints (原題)
Anonymous 2 Anonymous2
🤖 gxceed AI 要約
日本語
機械学習ワークロードのエネルギー消費を、モデル実行前に評価するゼロショットLCAフレームワークを提案。DAG走査とトポロジカル深度抽出により、運用炭素・Scope3組込炭素・ネットワーク伝送炭素・水フットプリントを統合的に推算する。実測テレメトリとの順位相関は高く、無線伝送の負荷がエッジ推論炭素を大幅に上回る場合があることを示した。
English
A zero-shot multi-scope LCA framework estimates ML workload footprints without executing models, using DAG traversal and topological depth extraction. It unifies operational carbon, Scope 3 embodied hardware carbon, network transmission carbon, and water footprints. Validation shows strong rank-order correlation with hardware telemetry, and wireless transmission can exceed edge-inference carbon by orders of magnitude.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
AI利用拡大に伴うGHG算定はSSBJ・Scope3開示の新たな論点であり、データセンター・クラウド調達の炭素管理に関わる日本企業にとって実務的示唆が大きい。
In the global GX context
As AI compute grows, quantifying its carbon and water footprints becomes central to ISSB/CSRD-aligned disclosure and green-AI claims, extending LCA into digital infrastructure accounting.
👥 読者別の含意
🔬研究者:AIワークロードのライフサイクル環境負荷を実行前に推定する手法として、持続可能なML設計研究に有用。
🏢実務担当者:クラウド・AI調達時の炭素・水フットプリント把握やScope3算定の精緻化に活用できる。
🏛政策担当者:AI・データセンターの環境開示ルール設計において、算定手法の標準化検討の参考になる。
📄 Abstract（原文）
Machine learning workloads now consume a significant and increasing percentage of global data centers energy. To overcome this problem, researchers and developers of new AI applications and pipelines are forced to use post hoc techniques and approaches to power telemetry to measure the actual power consumption during model executions. However, these approaches present a crucial and structural problem. Profiling a model requires running it first, which means we waste energy trying to measure its models’ energy’s draw. Furthermore, these tools and approaches are affected by ambient cloud virtualization noise rather than the actual algorithmic efficiencies. We propose a zero-shot, multiscope Life Cycle Analysis (LCA) framework that bypasses the actual physical execution of a model by recursively traversing a Directed Acyclic Graphs (DAGs) and extracting topological depth of the tree-based ensembles. The engine maps the graph-level arithmetic intensity to physical hardware saturation ceiling via dynamically rooflining modeling and profiling. Our proposed framework analytically bonds operational carbon, scope 3 embodied manufacturing which is type of carbon emitted during the different manufacturing process of hardware, carbon resulting from network transmission and its associated penalties, and finally direct/ indirect water footprints. The evaluation study was carried out on diverse set of modularity across vision, tabular and time series benchmarks on diver set of CPU and CPU accelerators, the static proposed approaches achieves a profiling latency of 0.05 ms to 0.08 ms, this represents a massive reduction compared to the 510.83S required for full physical execution of models. Validation against hardware telemetry confirms a strong rank-orders preservation (spearman’s p = 0.8333 to 0.9286) across modalities. A multi-scope projection demonstrates that wireless transmission footprint can in some cases exceed the edge-inference carbon emission by over six orders of magnitude. This framework allows researchers to perform sustainable architectural exploration before committing physical compute resources, eliminating hidden carbon taxes of green AI model profiling.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.23088165first seen 2026-10-03 04:59:59 · last seen 2026-10-03 05:00:01
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