Strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional Neural Networks

A new optimization technique to perform an embedded implementation of convolutional neural networks (CNN) using a strided convolution layers using the same filter size and stride of the old pooling layers to reduce the model size and improve the accuracy of a CNN.