Before revision:   Searching for the optimal mapping is challenging because of a massive space of possible data-layouts and loop transformations for the DNN layers. For example, there are over 10^19 valid mappingsfor a single convolution layer on average for mapping ResNet50 and MobileNetV2 on a representative DNN edge accelerator. This challenge gets exacerbated with new layer types (e.g., depth-wise and point-wise convolutions) and diverse hardware accelerator configurations.
After revision:  Searching for the optimal mapping is challenging because of the large space of mappings, and this challenge gets exacerbated with new layer types (e.g., depth-wise and point-wise convolutions) and diverse hardware accelerator configurations.