Xilinx, Inc.

Albert T Gural

Christopher H Dick

Michael Wu

Sambhav R Jain

Systems and methods for training a neural network model includes providing a quantization function including a quantization log threshold parameter associated with a log value of a quantization threshold. A quantization training to a neural network model is performed to generate quantized neural network parameters. The quantization training includes: generating first values with a first precision for the neural network parameters; performing a first optimization process to generate an updated quantization log threshold parameter; and generating quantized values with a second precision lower than the first precision for the neural network parameters by applying the quantization function with the updated quantization log threshold parameter to the first values. The neural network model with the quantized values for the neural network parameters is provided for performing a task.

System and method for implementing neural networks in integrated circuits

Method and system relating generally to convolution is disclosed. In such a method, an image patch is selected from input data for a first channel of a plurality of input channels of an input layer. The selected image patch is transformed to obtain a transformed image patch. The transformed image patch is stored. Stored is a plurality of predetermined transformed filter kernels. A stored transformed filter kernel of the plurality of stored predetermined transformed filter kernels is element-wise multiplied by multipliers with the stored transformed image patch for a second channel of the plurality of input channels different from the first channel to obtain a product. The product is inverse transformed to obtain a filtered patch for the image patch.

Growing community of inventors

Clifton, VA, United States of America

Albert T Gural