Xilinx, Inc.
Average pooling in a neural network

Abstract:

Embodiments herein describe, when executing an average pooling operation in a neural network, scaling input operands before performing an accumulate operation. Performing average pooling in a neural network averages the values in each face of a 3D volume, thereby downsampling or subsampling the data. This can be performed by adding all the values in a face and then dividing the total accumulated value by the total values in the face. However, the order of operations in a multiply-accumulator (MAC) is reversed from the order of operations for performing average pooling. To more efficiently use the MAC, the order of operations when performing average pooling is reversed so that determining the average value for a face can be performed on a single MAC. To do so, the values in the face are first scaled by a multiplier before being summed by an accumulator.