  Recently, research has increasingly focused on developing efficient neural
network architectures. In this work, we explore logic gate networks for machine
learning tasks by learning combinations of logic gates. These networks comprise
logic gates such as "AND" and "XOR", which allow for very fast execution. The
difficulty in learning logic gate networks is that they are conventionally
non-differentiable and therefore do not allow training with gradient descent.
Thus, to allow for effective training, we propose differentiable logic gate
networks, an architecture that combines real-valued logics and a continuously
parameterized relaxation of the network. The resulting discretized logic gate
networks achieve fast inference speeds, e.g., beyond a million images of MNIST
per second on a single CPU core.
