Neural networks have revolutionized the field of artificial intelligence, enabling machines to perform complex tasks once exclusive to human cognition. However, large-scale neural networks present significant computational challenges, particularly during training on servers and deployment on embedded devices. The high computational cost and resource demands impede their practical application in low-resources/energy devices. To address this issue, pruning is introduced, which is a technique that systematically removes redundant parameters and has emerged as a promising solution to reduce computational complexity while maintaining performance. This master's thesis explores the effectiveness of a novel layer, Multiply-And-Max/min (MAM), introduced as an alternative to the classical Multiply and Accumulate (MAC) approach, wherein the reduction function is not the sum of all elements but only of the largest and the smallest.