  A novel convolution neural network model, abbreviated NL-CNN is proposed,
where nonlinear convolution is emulated in a cascade of convolution +
nonlinearity layers. The code for its implementation and some trained models
are made publicly available. Performance evaluation for several widely known
datasets is provided, showing several relevant features: i) for small / medium
input image sizes the proposed network gives very good testing accuracy, given
a low implementation complexity and model size; ii) compares favorably with
other widely known resources-constrained models, for instance in comparison to
MobileNetv2 provides better accuracy with several times less training times and
up to ten times less parameters (memory occupied by the model); iii) has a
relevant set of hyper-parameters which can be easily and rapidly tuned due to
the fast training specific to it. All these features make NL-CNN suitable for
IoT, smart sensing, bio-medical portable instrumentation and other applications
where artificial intelligence must be deployed in energy-constrained
environments.
