DERIVING A CONCORDANT SOFTWARE NEURAL NETWORK LAYER FROM A QUANTIZED FIRMWARE NEURAL NETWORK LAYER

Systems and methods for deriving a concordant software neural network layer are provided. A method includes receiving first instructions configured to, using a neural network processor (NNP), process a first set of data corresponding to a neural network layer, where the NNP is configured to quantize...

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Bibliographic Details
Main Authors DANGWAL, Deeksha, FOWERS, Jeremy, LO, Daniel
Format Patent
LanguageEnglish
French
German
Published 05.01.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Systems and methods for deriving a concordant software neural network layer are provided. A method includes receiving first instructions configured to, using a neural network processor (NNP), process a first set of data corresponding to a neural network layer, where the NNP is configured to quantize the first set of the data to generate a set of quantized data and then perform matrix-vector multiply operations on the set of quantized data using a matrix-vector-multiplier incorporated within hardware associated with the NNP to generate a first set of results. The method further includes processing the first instructions to automatically generate second instructions configured for use with at least one processor, different from the NNP, such that the second instructions, when executed by the at least one processor to perform matrix multiply operations, generate a second set of results that are concordant with the first set of results.
Bibliography:Application Number: EP20200712747