Crossbar arrays implementing truth tables

A computer program product for training a crossbar array of a neural network, the computer program product comprising:

• one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions for:
• feeding an input portion of a predetermined truth table into a computer simulation of a crossbar array;
• generating analog output values for the input portion of the truth table based on simulated weights;
• calculating a loss value from each of the analog output values and expected values for an output portion of the truth table;
• adjusting the simulated weights based on the calculated loss values;
• and
• refeeding the input portion of the predetermined truth table into the computer simulation and recalculating the output values using the adjusted simulated weights until the analog output values produce the expected values for the output portion of the truth table within a predefined margin of error.

A computer system for preparing a trained crossbar array of a neural network, comprising:

• one or more processors;
• computer memory electronically coupled to the processors;
• a computer simulation, including a model of a crossbar array, wherein the computer simulation is configured to:
• receive an input portion of a predetermined truth table;
• generate analog output values for the input portion of the truth table based on simulated weights;
• calculate a loss value from each of the analog output values and expected values for an output portion of the truth table;
• adjust the simulated weights based on the calculated loss values;
• and
• refeed the input portion of the predetermined truth table into the computer simulation and recalculate the output values using the adjusted simulated weights until the analog output values produce the expected values for the output portion of the truth table within a predefined margin of error.