Approximating fully-connected layers with multiple arrays of 3x3 convolutional filter kernels in a cnn based integrated circuit

Abstract

Multiple 3×3 convolutional filter kernels are used for approximating operations of fully-connected (FC) layers. Image classification task is entirely performed within a CNN based integrated circuit. Output at the end of ordered convolutional layers contains P feature maps with F×F pixels of data per feature map. 3×3 filter kernels comprises L layers with each organized in an array of R×Q of 3×3 filter kernels, Q and R are respective numbers of input and output feature maps of a particular layer of the L layers. Each input feature map of the particular layer comprises F×F pixels of data with one-pixel padding added around its perimeter. Each output feature map of the particular layer comprises (F−2)×(F−2) pixels of useful data. Output of the last layer of the L layers contains Z classes. L equals to (F−1)/2 if F is an odd number. P, F, Q, R and Z are positive integers.