  We study the problem of formal verification of Binarized Neural Networks
(BNN), which have recently been proposed as a energy-efficient alternative to
traditional learning networks. The verification of BNNs, using the reduction to
hardware verification, can be even more scalable by factoring computations
among neurons within the same layer. By proving the NP-hardness of finding
optimal factoring as well as the hardness of PTAS approximability, we design
polynomial-time search heuristics to generate factoring solutions. The overall
framework allows applying verification techniques to moderately-sized BNNs for
embedded devices with thousands of neurons and inputs.
