  The notion of Boolean logic backpropagation was introduced to build neural
networks with weights and activations being Boolean numbers. Most of
computations can be done with Boolean logic instead of real arithmetic, both
during training and inference phases. But the underlying discrete optimization
problem is NP-hard, and the Boolean logic has no guarantee. In this work we
propose the first convergence analysis, under standard non-convex assumptions.
