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The current trend of applying transfer learning from convolutional neuralnetworks (CNNs) trained on large datasets can be an overkill when the targetapplication is a custom and delimited problem, with enough data to train anetwork from scratch. On the other hand, the training of custom and lighterCNNs requires expertise, in the from-scratch case, and or high-end resources,as in the case of hardware-aware neural architecture search (HW NAS), limitingaccess to the technology by non-habitual NN developers.  For this reason, we present ColabNAS, an affordable HW NAS technique forproducing lightweight task-specific CNNs. Its novel derivative-free searchstrategy, inspired by Occam's razor, allows to obtain state-of-the-art resultson the Visual Wake Word dataset, a standard TinyML benchmark, in just 3.1 GPUhours using free online GPU services such as Google Colaboratory and KaggleKernel.

Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor