JP6931712B2 - ニューラルネットワークを動作させる装置及び方法 - Google Patents
ニューラルネットワークを動作させる装置及び方法 Download PDFInfo
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- JP6931712B2 JP6931712B2 JP2019551437A JP2019551437A JP6931712B2 JP 6931712 B2 JP6931712 B2 JP 6931712B2 JP 2019551437 A JP2019551437 A JP 2019551437A JP 2019551437 A JP2019551437 A JP 2019551437A JP 6931712 B2 JP6931712 B2 JP 6931712B2
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- 238000013528 artificial neural network Methods 0.000 title claims description 83
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
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- G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
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Description
Claims (9)
- 複数のニューラルネットワークと、
前記複数のニューラルネットワークに結合されたコントローラと、
を備えた装置であって、
前記複数のニューラルネットワークの各ニューラルネットワークは、プロセッシングインメモリ(PIM)デバイス上の複数のバンクのうちの対応するバンク内で動作し、
各ニューラルネットワークは、
異なるトレーニングを受信し、
前記異なるトレーニングを受信するのに応答して、同時に特定のデータ部分の特徴を決定し、
前記複数のニューラルネットワークの各々の前記異なるトレーニングに基づいて、前記特定のデータ部分の前記特徴のトレーニング信頼係数を決定する、
ように構成され、
前記コントローラは、
投票方式を使用して、前記複数のニューラルネットワークの各々の前記トレーニング信頼係数に基づきデータ認識の精度を決定し、
前記データ認識の前記精度に基づき前記特定のデータ部分の前記特徴を決定する、
ように構成される、装置。 - 前記複数のニューラルネットワークは、センシング回路に結合されたメモリセルのアレイを含み、前記センシング回路は、センスアンプ及び計算コンポーネントを含む、請求項1に記載の装置。
- 前記メモリセルのアレイは、前記メモリセルのアレイと関連するデータパス計算動作におけるデータパスとして共有される複数の入力/出力(I/O)線を介して前記センシング回路に結合され、前記センシング回路が、前記特定のデータ部分の前記特徴についての前記決定を行うように構成される、、請求項2に記載の装置。
- 前記複数のニューラルネットワークは、独立してトレーニングされるように構成される、請求項1〜3のいずれか1項に記載の装置。
- 複数のニューラルネットワークと、
前記複数のニューラルネットワークに結合されたコントローラと、
を備えた装置であって、
前記複数のニューラルネットワークの各ニューラルネットワークは、プロセッシングインメモリ(PIM)デバイス上の複数のバンクのうちの対応するバンク内で動作し、
各ニューラルネットワークは、
異なるトレーニングを受信し、
特定のデータ部分を受信し、
前記異なるトレーニングを受信するのに応答して、同時に前記特定のデータ部分の特徴を決定し、
前記複数のニューラルネットワークの各々の前記異なるトレーニングに基づいて、前記特定のデータ部分の前記特徴のトレーニング信頼係数を決定する、
ように構成され、
前記コントローラは、
投票方式を使用して、前記複数のニューラルネットワークの各々の前記トレーニング信頼係数に基づきデータ認識の精度を決定し、
前記データ認識の前記精度に基づき前記特定のデータ部分の前記特徴を決定する、
ように構成される、装置。 - 前記コントローラは、前記複数のニューラルネットワークの各々から投票を受信するように構成される、請求項5に記載の装置。
- 前記複数のニューラルネットワークの各々からの前記投票は、前記複数のニューラルネットワークの各々の特定のデータ部分及び特定のトレーニングのタイプに基づき重み付けされる、請求項6に記載の装置。
- 複数のニューラルネットワークの各々を別々にトレーニングすることであって、前記複数のニューラルネットワークの各ニューラルネットワークは、プロセッシングインメモリ(PIM)デバイス上の複数のバンクのうちの対応するバンク内で動作する、ことと、
前記複数のニューラルネットワークの各々を別々にトレーニングすることに応答して、同時に、前記複数のニューラルネットワークの各々における特定のデータ部分の特徴を決定することと、
前記複数のニューラルネットワークの各々の前記別々のトレーニングに基づいて、前記特定のデータ部分の前記特徴のトレーニング信頼係数を決定することと、
コントローラにて、投票方式を使用して、前記複数のニューラルネットワークの各々の前記トレーニング信頼係数に基づきデータ認識の精度を決定することと、
前記コントローラにて、前記データ認識の前記精度に基づき前記特定のデータ部分の前記特徴を決定することと、
を含む方法。 - 複数のニューラルネットワークの各々を別々にトレーニングすることであって、前記複数のニューラルネットワークの各ニューラルネットワークは、プロセッシングインメモリ(PIM)デバイス上の複数のバンクのうちの対応するバンク内で動作する、ことと、
前記複数のニューラルネットワークの各々を別々にトレーニングすることに応答して、同時に前記複数のニューラルネットワークを動作させることであって、前記複数のニューラルネットワークの各々は、特定のデータ部分を受信し、前記特定のデータ部分の特徴を決定し、かつ、前記複数のニューラルネットワークの各々の前記別々のトレーニングに基づき前記特定のデータ部分の前記特徴のトレーニング信頼係数を決定する、ことと、
コントローラを動作させることであって、前記コントローラは、投票方式を使用して、前記複数のニューラルネットワークの各々の前記トレーニング信頼係数に基づきデータ認識の精度を決定し、前記コントローラは、前記データ認識の前記精度に基づき前記特定のデータ部分の前記特徴を決定する、ことと、
を含む方法。
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US15/466,296 US11222260B2 (en) | 2017-03-22 | 2017-03-22 | Apparatuses and methods for operating neural networks |
PCT/US2018/021936 WO2018175130A1 (en) | 2017-03-22 | 2018-03-12 | Apparatuses and methods for operating neural networks |
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