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US Patent 11531727 Computation of neural network node with large input values

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
115317270
Patent Inventor Names
Jung Ko0
Steven L. Teig0
Kenneth Duong0
Date of Patent
December 20, 2022
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Patent Application Number
162126450
Date Filed
December 6, 2018
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Patent Citations
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US Patent 10489478 Configurable convolution engine
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US Patent 10515303 Wavelet representation for accelerated deep learning
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US Patent 10657438 Backpressure for accelerated deep learning
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US Patent 10768856 Memory access for multiple circuit components
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US Patent 10796198 Adjusting enhancement coefficients for neural network engine
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US Patent 11250326 Splitting neural network filters for implementation by neural network inference circuit
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US Patent 11138292 Circuit and method for computing depthwise convolution
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US Patent 11295200 Time-multiplexed dot products for neural network inference circuit
...
Patent Citations Received
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US Patent 12131251 Neurons for artificial neural networks
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Patent Primary Examiner
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Michael D. Yaary
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CPC Code
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G06N 3/063
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G06N 3/06
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G06F 5/01
0
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G06F 7/5443
0
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G06F 17/16
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G06F 9/30098
0

Some embodiments provide a method for a circuit that executes a neural network including multiple nodes. The method loads a set of weight values for a node into a set of weight value buffers, a first set of bits of each input value of a set of input values for the node into a first set of input value buffers, and a second set of bits of each of the input values into a second set of input value buffers. The method computes a first dot product of the weight values and the first set of bits of each input value and a second dot product of the weight values and the second set of bits of each input value. The method shifts the second dot product by a particular number of bits and adds the first dot product with the bit-shifted second dot product to compute a dot product for the node.

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