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Recently, in the paper "Weight Agnostic Neural Networks" Gaier & Ha utilizedarchitecture search to find networks where the topology completely encodes theknowledge. However, architecture search in topology space is expensive. We usethe existing framework of binarized networks to find performant topologies byconstraining the weights to be either, zero or one. We show that suchtopologies achieve performance similar to standard networks while pruning morethan 99% weights. We further demonstrate that these topologies can performtasks using constant weights without any explicit tuning. Finally, we discoverthat in our setup each neuron acts like a NOR gate, virtually learning adigital circuit. We demonstrate the efficacy of our approach on computer visiondatasets.

Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks