System and methods for mixed-signal computing

A method comprising:

• generating, by a global digital-to-analog converter, one or more analog reference signals based on digital input data;
• supplying the one or more analog reference signals to a plurality of local digital-to-analog converters, each local digital-to-analog converter having an energy storage device;
• receiving, at each of the plurality of local digital-to-analog converters, a respective one of a plurality of biasing inputs that directs incrementing or decrementing of electrical charge stored on the corresponding energy storage device;
• accumulating, by each of the plurality of local digital-to-analog converters, electrical charge on the corresponding energy storage device over a plurality of clock cycles to generate an analog output;
• applying, to each of a plurality of neuron columns, the analog outputs generated by the plurality of local digital-to-analog converters, each neuron column comprising a plurality of tunable resistors;
• and
• generating, by each neuron column, a respective neuron output by aggregating electrical currents produced by the plurality of tunable resistors in the neuron column.

A neural computing circuit comprising:

• a plurality of input terminals configured to receive a plurality of analog input signals;
• a plurality of tunable resistors, each tunable resistor being electrically connected to a respective one of the plurality of input terminals and configured to generate an electrical current based on the corresponding analog input signal;
• a current summation node electrically connected to the plurality of tunable resistors, the current summation node being configured to receive the electrical currents generated by the plurality of tunable resistors;
• and
• an output terminal electrically connected to the current summation node and configured to provide a neuron output corresponding to a sum of the electrical currents generated by the plurality of tunable resistors.