Methods and apparatuses for high performance and accuracy fixed-point batchnorm implementation

An apparatus for implementing a neural network, comprising:

• one or more processors;
• and
• a memory configured to store instructions executable by the one or more processors;
• wherein the one or more processors, upon execution of the instructions, are configured to:
• receive floating-point input data over a channel of a standalone floating-point batchnorm layer in a neural network, and convert the floating-point input data into fixed-point input data of the standalone floating-point batchnorm layer;
• obtain fixed-point quantization parameters in each channel based on input data and a plurality of floating-point parameters in each channel;
• convert the standalone floating-point batchnorm layer based on the fixed-point quantization parameters into a fixed-point batchnorm layer in the neural network;
• process the fixed-point input data to generate fixed-point output data, wherein the fixed-point output data is generated by right shifting with rounding and claiming the fixed-point input data on the fixed-point batchnorm layer;
• and
• map the fixed-point batchnorm layer to a fixed-point convolution layer, wherein computation of convolution is done by matrix multiplication executed on a GEMM engine.

A non-transitory computer readable storage medium, comprising instructions stored therein to implement a neural network, wherein, upon execution of the instructions by one or more processors, the instructions cause the one or more processors to perform acts comprising:

• receiving floating-point input data over a channel of a standalone floating-point batchnorm layer in a neural network, and converting the floating-point input data into fixed-point input data of the standalone floating-point batchnorm layer;
• obtaining fixed-point quantization parameters in each channel based on input data and a plurality of floating-point parameters in each channel;
• converting the standalone floating-point batchnorm layer based on the fixed-point quantization parameters into a fixed-point batchnorm layer in the neural network;
• processing the fixed-point input data to generate fixed-point output data, wherein the fixed-point output data is generated by right shifting with rounding and claiming the fixed-point input data on the fixed-point batchnorm layer;
• and
• mapping the fixed-point batchnorm layer to a fixed-point convolution layer, wherein computation of convolution is done by matrix multiplication that executed on a GEMM engine.