11 光学ニューラルネットワークのためのTOPS光コンボリューション加速器
Xingyuan Xu1,2, Mengxi Tan1, Bill Corcoran3
1Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Nature
|January 7, 2021
まとめ
10テラオプス/秒を超える光学収束型ニューラルネットワーク加速器を開発しました この新しいシステムは 88%の精度で 手書きの数字を認識し AIのアプリケーションのスピードを 向上させています
科学分野:
- 人工知能
- 光学コンピューティング
- 機械学習
背景:
- コンボリューションニューラルネットワーク (CNN) は 機能抽出のための強力なAIツールで コンピュータビジョンや医療診断などのタスクに不可欠です
- 従来の電子CNNは,速度と電力消費に制限があります.
- 光学ニューラルネットワークは,光学帯域幅を活用して,加速コンピューティングのための有望な道を提供します.
研究 の 目的:
- 普遍的な光学ベクトル収縮加速器を演示する.
- 超高速の画像認識を 光学コンボリューションニューラルネットワークで実現する
- 複雑なAIタスクのための統合光学システムの可能性を探求する.
主な方法:
- 秒速10テラオプス以上で動作する光学ベクトルコンボリューション加速器の開発.
- 時間,波長,空間的な次元を交互に 統合したマイクロコンブソースを使用した.
- 画像認識のタスクのための連続的な光学コンボリューションニューラルネットワークを形成するためにハードウェアを構成します.
主要な成果:
- 顔認識に適した25万ピクセルの画像を処理できる光学アクセラレータを実証しました
- 88%の精度で 手書きの画像を認識しました
- このシステムは 秒速10テラオプスを超える速度で動作します
結論:
- 開発された光学収束型ニューラルネットワークアクセラレータは,高性能AIのためのスケーラブルで訓練可能なプラットフォームを提供します.
- この技術は 自動運転車やリアルタイムビデオ認識などの 要求の高いアプリケーションに 大きな可能性を秘めています
- マイクロコムソースを使用した統合的なアプローチは,複数の次元を交互に繋げることで効率的な処理を可能にします.
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