Neural learning in analogue hardware: effects of component variation from fabrication and from noise

Simulations of backpropagation networks in analogue circuitry with on-chip learning are presented. Forward and backward computations are performed using Gilbert multipliers. Component variations fixed in fabrication are shown to be adapted to and tolerated much better than similar variations due to noise.