  The Softmax activation layer is a very popular Deep Neural Network (DNN)
component when dealing with multi-class prediction problems. However, in DNN
accelerator implementations it creates additional complexities due to the need
for computation of the exponential for each of its inputs. In this brief we
propose a simplified version of the activation unit for accelerators, where
only a comparator unit produces the classification result, by choosing the
maximum among its inputs. Due to the nature of the activation function, we show
that this result is always identical to the classification produced by the
Softmax layer.
