Stacking multiple small convolutions provides the same receptive field as a single larger convolution while improving parameter efficiency and increasing depth. For example, applying two successive $3 imes 3$ convolutions touches the same input pixels as a single $5 imes 5$ convolution. However, while a single $5 imes 5$ convolution requires $25 \cdot c^2$ parameters (where $c$ is the number of channels), three successive $3 imes 3$ convolutions use a comparable number of parameters ($3 \cdot 9 \cdot c^2 = 27 \cdot c^2$) while providing an even larger receptive field. This analysis demonstrated that deep and narrow networks significantly outperform their shallow and wide counterparts, establishing stacked $3 imes 3$ convolutions as a gold standard in deep learning architecture design.