Logic Gate Networks Generated Using Differentiable Logic Gate Models

A method for training a logic gate neural network, comprising:

• receiving, at a computing system, a training data set of input vectors and corresponding target output values;
• instantiating, in a memory of the computing system, an untrained logic gate network with a plurality of nodes, wherein each node is parameterized by a set of differentiable parameters corresponding to a predefined set of potential logic gate operators;
• iteratively training the logic gate network via a plurality of training iterations, each training iteration including:
• forward-propagating a batch of the input vectors through the logic gate network to generate a training network output by, for each node, computing a differentiable output that is a function of the outputs of the potential logic gate operators of the respective node according to current differentiable parameters thereof;
• computing a loss value that quantifies a difference between the training network output and the corresponding target output values;
• determining, via a training optimization algorithm, updated differentiable parameters for at least one node;
• and
• applying the updated differentiable parameters to at least one node;
• and
• generating, after completion of the plurality of training iterations, a fixed logic gate network by identifying a single logic gate from the set of potential logic gate operators for a plurality of the nodes based on the differentiable parameters thereof.