スケーラブルニューロモルフィックコンピューティングのためのイオン浮動ゲートメモリ配列の並列プログラミング
Elliot J Fuller1, Scott T Keene2, Armantas Melianas2
1Sandia National Laboratories, Livermore, CA, USA.
Science (New York, N.Y.)
|April 27, 2019
まとめ
研究者はニューロモルフィックコンピューティングのための新しいイオン浮動ゲートメモリ配列を開発しました. このシステムは効率的な平行重量アップデートと 低電流の読み取りを可能にし 人工知能のハードウェアを進歩させています
科学分野:
- 材料科学
- コンピュータ工学
- 神経科学
背景:
- 従来のコンピューティングは,特に人工ニューラルネットワークでは,効率の限界に直面しています.
- ニューロモルフィックコンピューティングは,並列処理と特殊なメモリを通じて効率を改善することを目的としています.
- 効率的な学習を達成するには,選択的で線形的な重量アップデートと低読み電流 (<10 nA) が必要です.
研究 の 目的:
- 新しいイオン浮遊ゲートメモリ配列を導入し 効率的な神経形コンピューティングを行います
- 人工ニューラルネットワークの重さの選択的および線形プログラミングを実証する.
- 10ナノアンペア未満の電流でシナプス重量読み取りを達成する.
主な方法:
- 導電ブリッジメモリ (CBM) と統合されたポリマーリドックストランジスタを使用した.
- 選択的な重量アップデートのためのCBMブリッジングスリーフ電圧を克服することによって並列プログラミングを実行します.
- 導電性ポリマーを絶縁剤で薄めることで低読電流が得られる.
主要な成果:
- レドックストランジスタ配列の選択的および線形プログラミングを並行して実証した.
- 10ナノアンペア未満の電流でシナプス重量読み取りを達成しました.
- メモリ配列は高い耐久性 (>10億回) と高い周波数 (>1MHz) を示した.
結論:
- 開発されたイオン浮遊ゲートメモリ配列は 効率的な神経形学習の重要な要件を満たしています
- この技術は AI アプリケーションで従来のコンピューティング効率を 超越する道を示しています
- このシステムは頑丈で高速な ニューロモルフィックハードウェアを 実現する見込みです
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