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Capacitor based resistive processing unit with symmetric weight update

專利號
US11176451B2
公開日期
2021-11-16
申請人
INTERNATIONAL BUSINESS MACHINES CORPORATION(US NY Armonk)
發(fā)明人
Yulong Li; Paul M. Solomon; Effendi Leobandung
IPC分類
G06N3/063; G06N3/08; G11C11/24; G11C11/54
技術領域
capacitor,charge,first,gates,second,voltage,weight,analog,pre,stochastic
地域: NY NY Armonk

摘要

Systems and methods for a capacitor based resistive processing unit with symmetrical weight updating include a first capacitor that stores a charge corresponding to a weight value. A readout circuit reads the charge stored in the first capacitor to apply a weight to an input value corresponding to an input signal using the weight value to produce an output. An update circuit updates the weight value stored in the first capacitor, including a second capacitor in communication with the first capacitor to transfer an amount of charge to the first capacitor according to an error of the output by changing a voltage difference across the first capacitor by a voltage change corresponding to the amount of charge, the voltage difference corresponding to the charge stored in the first capacitor.

說明書

In accordance with an embodiment of the present invention, a capacitor based resistive processing unit with symmetrical weight updating is described. The resistive processing unit includes a first capacitor that stores a charge corresponding to a weight value. A readout circuit reads the charge stored in the first capacitor to apply a weight to an input value corresponding to an input signal using the weight value to produce an output. An update circuit updates the weight value stored in the first capacitor, including a second capacitor in communication with the first capacitor to transfer an amount of charge to or from the first capacitor according to an error of the output by changing a voltage difference across the first capacitor by a voltage change corresponding to the amount of charge, the voltage difference corresponding to the charge stored in the first capacitor. The error of the output can be calculated by machine learning algorithms such as stochastic gradient descend and back propagation.

權利要求

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