Convolutional Neural Network-based (CNN) inference is a demanding computational task where a long
sequence of operations is applied to an input as dictated by the network topology. Optimisations
by data quantisation, data reuse, network pruning, and dedicated hardware architectures have a
strong impact on reducing both energy consumption and hardware resource requirements, and on
improving inference latency. Implementing new applications from established models available from
both academic and industrial worlds is common nowadays. Further optimisations by preserving model
architecture have been proposed via early exiting approaches, where additional exit points are included
in order to evaluate classifications of samples that produce feature maps with sufficient evidence to
be classified before reaching the final model exit. This paper proposes a methodology for designing
early-exit networks from a given baseline model aiming to improve the average latency for a targeted
subset class constrained by the original accuracy for all classes. Results demonstrate average time
saving in the order of 2.09× to 8.79× for dataset CIFAR10 and 15.00× to 20.71× for CIFAR100 for
baseline models ResNet-21, ResNet-110, Inceptionv3-159, and DenseNet-121.