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US Patent 10360971 Artificial neural network functionality within dynamic random-access memory

Patent 10360971 was granted and assigned to Green Mountain Semiconductor, Inc. on July, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Green Mountain Semiconductor, Inc.
Green Mountain Semiconductor, Inc.
Current Assignee
Green Mountain Semiconductor, Inc.
Green Mountain Semiconductor, Inc.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10360971
Date of Patent
July 23, 2019
Patent Application Number
15961599
Date Filed
April 24, 2018
Patent Citations Received
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US Patent 12093807 Neural network, method of control of neural network, and processor of neural network
0
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US Patent 11423290 Methods of executing an arithmetic operation and semiconductor devices performing the arithmetic operation
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US Patent 11450364 Computing-in-memory architecture
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US Patent 12026601 Stacked artificial neural networks
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US Patent 11526285 Memory device for neural networks
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US Patent 10860924 Hardware node having a mixed-signal matrix vector unit
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US Patent 11244718 Control of NAND flash memory for al applications
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US Patent 11593002 Artificial neural networks in memory
...
Patent Primary Examiner
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Vu A Le
Patent abstract

Techniques are disclosed for artificial neural network functionality within dynamic random-access memory. A plurality of dynamic random-access cells is accessed within a memory block. Data within the plurality of dynamic random-access cells is sensed using a plurality of sense amplifiers associated with the plurality of dynamic random-access cells. A plurality of select lines coupled to the plurality of sense amplifiers is activated to facilitate the sensing of the data within the plurality of dynamic random-access cells, wherein the activating is a function of inputs to a layer within a neural network, and wherein a bit within the plurality of dynamic random-access cells is sensed by a first sense amplifier and a second sense amplifier within the plurality of sense amplifiers. Resulting data is provided based on the activating wherein the resulting data is a function of weights within the neural network.

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