A continuously-adapting analog node using floating-gate synapses

Demonstrates that a network of pFET single-transistor learning synapses implements a multi-input adaptive node. These floating-gate synapses are capable of four-quadrant multiplications between an input and a weight, as well as adapting to a four-quadrant correlation between the input and a learning signal applied as a drain voltage. Our adaptive floating-gate node structure converges to the correct solution for constant RMS input values during adaptation. Our structure accounts for device mismatch, gate variance, and drain variance effects in the learning rule. We present experimental results for our circuit.