自動学習機能を持つ全光学的スパイク神経シナプスネットワーク
J Feldmann1, N Youngblood2, C D Wright3
1Institute of Physics, University of Münster, Münster, Germany.
Nature
|May 10, 2019
まとめ
研究者は脳にインスパイアされた ニューロシナプス系を 開発しました この光子ニューラルネットワークは パターン認識を証明し より速く より効率的な光学データ処理を可能にします
科学分野:
- 神経科学
- コンピュータ科学
- 光学について
背景:
- 伝統的なコンピューティングはメモリと処理を分離し 速度とエネルギー効率を制限します
- 脳にインスパイアされたコンピューティングと ニューロモルフィックシステムは より効率的な代替手段を 提供します
- 現在のニューロモルフィックシステムは 電子部品に依存しています
研究 の 目的:
- 脳にインスパイアされたコンピューティングのための 全光ニューロシナプスシステムです
- フォトニックシステムにおける監督および無監督の学習能力を実証する.
- 電気通信と視覚データのための直接的な光学データ処理を可能にします.
主な方法:
- 拡張可能な光子ニューラルネットワークのアーキテクチャのための波長分割マルチプレキシングを使用した.
- 生物学的ニューロンとシナプスを模倣した 全光神経シナプスシステムを開発した
- 直接光学領域内でパターンを認識する.
主要な成果:
- フォトニック神経シナプスネットワークを使って パターン認識を成功裏に実証しました
- 完全光学システムで 監督・無監督の学習を実現します
- 光学データの直接処理の可能性を示した.
結論:
- フォトニック神経シナプスネットワークは 高速で低エネルギーコンピューティングに 有望なアプローチを提供します
- この技術は,光通信と視覚データの直接処理を可能にします.
- 開発されたシステムは,光学的ニューロモルフィックコンピューティングの分野を前進させる.
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