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US 11,836,607 B2Granted
System and method for classifying data using neural networks with errors
AssigneeIntegrated Silicon Solution, (Cayman) Inc.
InventorMichail Tzoufras
Life of this patent
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Abstract
A computing device includes one or more processors, random access memory (RAM), and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors. The computing device receives first data and classifies the first data using a neural network that includes at least one quantized layer. The classifying includes reading values from the random access memory for a set of weights of the at least one quantized layer of the neural network using first read parameters corresponding to a first error rate.
Claims20
A method, comprising:
- performing, at a computing device that includes one or more processors, a random access memory (RAM), and a non-transitory computer-readable storage medium including instructions for execution by the one or more processors, a set of operations including:
- receiving first data;
- and
- classifying the first data using a neural network that includes at least one quantized layer, wherein the classifying includes reading values from the random access memory for a set of weights of the at least one quantized layer of the neural network using first read parameters corresponding to a first error rate, wherein a percentage of the weights for the entire neural network within a range of 50%-90% are binary weights.
1 more element
An electronic system, comprising:
- one or more processors;
- a random access memory (RAM);
- read circuitry configured to read data from the RAM;
- and
- a non-transitory computer-readable storage medium including instructions for execution by the one or more processors, a set of operations including:
- receiving first data;
- and
- classifying the first data using a neural network that includes at least one quantized layer, wherein the classifying includes reading values from the random access memory for a set of weights of the at least one quantized layer of the neural network using first read parameters corresponding to a first error rate, wherein a percentage of the weights for the entire neural network within a range of 50%-90% are binary weights.
5 more elements
Dependencies are inferred from the claim text.