スピントークナノ振動器を4つ組み合わせた母音認識
Miguel Romera1, Philippe Talatchian1, Sumito Tsunegi2
1Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.
Nature
|October 31, 2018
まとめ
この研究は,スピントロニックナノ振動器を用いた人工知能 (AI) ハードウェアの新しい方法を実証しています. このダイナミックなシステムは 音声のような複雑なパターンを 学習し 認識することができます
科学分野:
- 神経科学と人工知能
- スピントロニクスとナノ電子
背景:
- 人工ニューラルネットワーク (ANN) は通常,静的な関数と標準的な算数を使用します.
- 神経系にインスパイアされたコンピューティングは ダイナミックなシステムを模倣し 複雑な問題を解決するために 振動や同期などの脳の機能を模倣します
- 新興のナノ電子デバイスは,ハードウェア実装のためのコンパクトでエネルギー効率の良い非線形オートオシレータを提供しています.
研究 の 目的:
- ダイナミックなナノ電子ニューラルネットワークにおける学習の課題に取り組むこと
- ハードウェアでの学習を達成するために,スピントロニックナノオシレータの調節性を使用することを実証する.
- 小規模でダイナミックなニューラルネットワークの パターン分類能力を示します
主な方法:
- 4つのスピン・トルク・ナノ・オシレータの ハードウェア・ネットワークを利用した
- オシレータの周波数を調整する 自動リアルタイム学習ルールを採用した.
- 計算のための結合振動器の同期現象を活用した.
主要な成果:
- ナノ振動器のネットワークを 音声認識に成功させました
- 振動器同期による高い実験認識率を達成した.
- 非線形ダイナミック特性が,小型のハードウェアネットワークにおける非微妙なパターンの分類を可能にすることを示した.
結論:
- スピントロニックナノオシレータの幅広い調節性は,ダイナミックなニューロモルフィックハードウェアでの学習を可能にする鍵です.
- 結合されたナノ振動器の同期は複雑な計算作業を容易にする.
- このアプローチは エネルギー効率の良い コンパクトな AI ハードウェアへの道を開きます
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