Scalable Switch Capacitor Computation Cores for Accurate and Efficient Deep Learning Inference

An apparatus comprising:

• a first plurality of inputs representing an original activation input vector;
• a plurality of voltage multipliers that take the said first plurality of inputs and produce a second plurality of inputs by multiplying at least one scaling factor to voltages of the original activation input vector;
• a third plurality of inputs representing a weight input vector;
• an analog multiplier-and-accumulator to generate an analog voltage representing a multiply-and-accumulate result for the said second inputs and the third inputs;
• an analog to digital converter configured to convert the said analog voltage multiply-and-accumulate result into a digital signal using a limited-precision operation during a neural network inference operation;
• and
• a hardware controller configured to determine the at least one scaling factor based on the multiply-and-accumulate result, or a software controller configured to determine the at least one scaling factor based on the multiply-and-accumulate result.

A method comprising:

• receiving a first plurality of inputs representing an activation input vector;
• receiving a second plurality of inputs representing a weight input vector;
• generating, with an analog multiplier-and-accumulator, an analog voltage representing a multiply-and-accumulate result for the first plurality of inputs and the second plurality of inputs;
• converting, with an analog to digital converter, the analog voltage multiply-and-accumulate result into a digital signal using a limited-precision operation during an inference operation of a neural network;
• and
• determining, during training or calibration of the neural network, at least one scaling factor used to amplify the first plurality of inputs or to amplify the analog voltage multiply-and-accumulate result.