Systems and methods for analog ai deep learning

A system for an analog neural network comprising:

• a digital-to-analog converter (DAC);
• a programming module that provides programming via control signals to train and set weight values in an analog form for programmable nodes of an analog crossbar array network;
• the analog crossbar array network that comprises a crossbar array block, switching rows and switching columns;
• the crossbar array block that comprises a matrix of the programmable nodes, which supports asynchronous neural network training utilizing parallel processing;
• the switching rows and the switching columns that receive respective data signals from the DAC and control signals from the programming module, and output respective switched data to the matrix of the programmable nodes of the crossbar array block;
• an analog-to-digital converter (ADC) that generates a digital signal based on an analog neural network output signal received from the switching rows and switching columns of the crossbar array;
• and
• a digital system that provides a DAC code to the DAC and provides a data signal to the programming module, and receives the digital signal from the ADC.