This paper aims to provide an automatic tool that embeds the knowledge required to design hardware-aware neural networks to allows their implementation on microcontrollers. An algorithm called AUTONET is developed, which, given an input dataset and memory constraints of the target hardware, and aided by network design rules, generates autonomously an over-parameterized network intrinsically optimized for memory, to be used as the search space within a differential type NAS. The search space found by AUTONET on CIFAR10 and CIFAR100 datasets generates networks that are on average 95% smaller and 68% faster with respect to the ones found using MobileNetV2 as a backbone, being able also to compete in the Visual Wake Word dataset challenge obtaining an accuracy of 86.47%.