Improved correlation learning rule in continuously adapting floating-gate arrays using logarithmic pre-distortion of input and learning signals

Floating-gate pFETs can be used to implement analog signal multiplication with an adaptive analog gain, creating the possibility for compact, low-power analog adaptive filters and neural networks in VLSI. We have shown that the adaptation mechanism of this device yields a weight which is proportional to correlations between terminal voltage signals. However, the weight contains dependencies proportional to the variances of these terminal voltages as well. In addition, harmonic distortion of the input signals can mask thin correlation effect. In this paper we show how to eliminate or minimize these non-idealities through logarithmic pre-distortion of our input and learning signals as well as through the use of differential circuit structures. The result is a weight update mechanism which is closer to a pure correlation learning rule