Method to map convolutional layers of deep neural network on a plurality of processing elements with SIMD execution units, private memories, and connected as a 2D systolic processor array
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United States of America
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Nov 12, 2024
Grant Date -
Apr 30, 2020
app pub date -
Oct 31, 2018
filing date -
Oct 31, 2018
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Abstract
A method for improving performance of a predefined Deep Neural Network (DNN) convolution processing on a computing device includes inputting parameters, as input data into a processor on a computer that formalizes a design space exploration of a convolution mapping, on a predefined computer architecture that will execute the predefined convolution processing. The parameters are predefined as guided by a specification for the predefined convolution processing to be implemented by the convolution mapping and by a microarchitectural specification for the processor that will execute the predefined convolution processing. The processor calculates performance metrics for executing the predefined convolution processing on the computing device, as functions of the predefined parameters, as proxy estimates of performance of different possible design choices to implement the predefined convolution processing.
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