Machine-learning classifier based on comparators for direct inference on analog sensor data

A method of performing classification using a strong binary classifier, the method comprising:

• generating a plurality of weak classification outputs via a plurality of weak binary classifiers, each generation of a weak classification output comprising:
• generating a weighted input signal from a received input signal for classification via a plurality of weighting amplifier stages;
• generating a positive input signal for classification based on positive-weighted input signals generated from the plurality of weighting amplifier stages via a positive summing node;
• generating a negative input signal for classification based on negative-weighted input signal generated from the plurality of weighting amplifier stages via a negative summing node;
• comparing a sum of the positive-weighted input signal and a sum of the negative-weighted input signal via a comparator;
• generating a strong classification output based on the plurality of weak classification outputs via a digital voter;
• and
• iteratively training the plurality of weak binary classifiers via a trainer based on incorporating a non-convex optimization constraint reformulated to be solvable via quadratic programming into generating the weighted input signals to constrain a precision of the weights applied to the input signals.

A weak binary classifier configured to receive a plurality of input signals for classification and generate a weak classification output, the weak binary classifier comprising:

• a plurality of weighting amplifier stages, each weighting amplifier stage configured to receive an input signal for classification and generate a weighted input signal;
• a positive summing node and a negative summing node coupleable to the plurality of weighting amplifier stages, the positive summing node configured to generate a positive input signal for classification based on positive-weighted input signals generated from the plurality of weighting amplifier stages, the negative summing node configured to generate a negative input signal for classification based on negative-weighted input signals generated from the plurality of weighting amplifier stages;
• and
• a comparator coupled to the positive summing node and negative summing node, the comparator configured to compare a sum of the positive-weighted input signals and a sum of the negative-weighted input signals to generate a weak classification output;
• the weak binary classifier configured to be iteratively trained based on incorporating a non-convex optimization constraint reformulated to be solvable via quadratic programming into generating the weighted input signals to constrain a precision of the weights applied to the input signals.