テンソールコンパイラのためのコンフィギュレーションクロスアテンションを持つグラフニューラルネットワーク
Dmitrii Khizbullin1, Eduardo Rocha de Andrade2, Thanh Hau Nguyen2
1King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Frontiers in artificial intelligence
|September 5, 2025
まとめ
TGraphは,テンソール構成をスマートにスクリーニングすることで,ニューラルネットワークの推論を加速します. このAIテンソールコンパイラは AIデータセンターのパフォーマンスを大幅に改善し エネルギー消費量を削減します
科学分野:
- 人工知能
- コンピュータ科学
- 機械学習
背景:
- 神経ネットワークの推論には,テンソール演算の効率的な計算が必要です.
- テンソールレイアウト (転置,タイリング) を最適化することは,組み合わせ上複雑である.
- 既存のコンパイラはしばしばヒューリスティックに依存し,パフォーマンスの向上を制限します.
研究 の 目的:
- ニューラルネットワークの推論を最適化するための新しいニューラルグラフアーキテクチャであるTGraphを紹介します.
- AIテンソルコンパイラを開発し,高速テンソル構成をスクリーニングする.
- AI推論の計算コストとエネルギーフットプリントを削減します
主な方法:
- ニューラルネットワークの推論を計算グラフとして表現する.
- グラフアーキテクチャを利用してテンソールレイアウトの並び替えを探求する.
- 伝統的なアプローチとは異なるAIテンソールコンパイラフレームワークの開発.
主要な成果:
- TGraphは,レイアウトコレクションの平均Kendallの τを大幅に改善し,ベースラインの29.8%と比較して67.4%に達しました.
- 提案された方法はAIテンソールコンパイラとして機能し,ヒューリスティックベースの方法を上回ります.
- AIデータセンター地域における家計の排出量の50%以上に相当する潜在的CO2排出量削減
結論:
- TGraphはニューラルネットワークの推論を最適化するための強力な新しいアプローチを提供します.
- AIテンソールコンパイラフレームワークは,実質的なパフォーマンスの改善を示しています.
- この研究は,人工知能のエネルギー効率に重大な影響を及ぼします.
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