統合フォトニックテンソールコアを使用した並列コンボリューション処理
J Feldmann1, N Youngblood2,3, M Karpov4
1Institute of Physics, University of Münster, Münster, Germany.
Nature
|January 7, 2021
まとめ
光学ハードウェアの加速器である フォトニックテンソール・コアを開発し 1秒間に何兆もの 増量-蓄積操作を実行しました この統合されたフォトニックデバイスは データ密集型アプリケーションに より高速でスケーラブルな AI ハードウェアへの道を開きます
科学分野:
- 統合フォトニクス
- 光学コンピューティング
- 人工知能のハードウェア
背景:
- モバイルネットワーク,IoT,AIからの指数関数的なデータ成長は より高速で効率的なハードウェアを必要とします
- 大量のデータセットを処理するためのスピードとスケーラビリティの現在のハードウェアの制限.
- コンピューティング密集したAIタスクのための特殊なハードウェアアクセラレータの必要性
研究 の 目的:
- コンピューティングに特化した統合フォトニックハードウェア加速器 (テンサー・コア) を実証する.
- フォトニック技術を用いた高速で並列化されたインメモリコンピューティングを実現する.
- 未来の人工知能のハードウェアに 統合されたフォトニクスの可能性を 探求すること
主な方法:
- フェーズチェンジマテリアルメモリ配列を用いた光子テンサーコアを開発した.
- 計算のために光子チップベースの光学周波数コンブ (ソリトンマイクロコンブ) を使用しています.
- 再構成可能なパッシブコンポーネントを介して光学伝送を測定する計算を減らす.
主要な成果:
- 1秒間に何兆もの倍加-蓄積操作 (テラ-MACs/s) の動作速度を達成した.
- 計算帯域幅が14ギガヘルツを超え,調節器と光検出器の速度によって制限されています.
- フォトニック・テンソール・コアのCMOS・ウェーファー・スケール・インテグレーションへの道を示した.
結論:
- 光学コンピューティングの 重要な進歩です
- 統合フォトニクスは,並列,高速,効率的なAI計算のための有望なソリューションを提供します.
- この技術は自動運転,ライブビデオ処理,クラウドコンピューティングに 応用できる可能性があります
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