  Convolutional Neural Networks have achieved unprecedented success in image
classification, recognition, or detection applications. However, their
large-scale deployment in embedded devices is still limited by the huge
computational requirements, i.e., millions of MAC operations per layer. In this
article, MinConvNets where the multiplications in the forward propagation are
approximated by minimum comparator operations are introduced. Hardware
implementation of minimum operation is much simpler than multipliers. Firstly,
a methodology to find approximate operations based on statistical correlation
is presented. We show that it is possible to replace multipliers by minimum
operations in the forward propagation under certain constraints, i.e. given
similar mean and variances of the feature and the weight vectors. A modified
training method which guarantees the above constraints is proposed. And it is
shown that equivalent precision can be achieved during inference with
MinConvNets by using transfer learning from well trained exact CNNs.
