Patent ID: 11915132
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An artificial neural network system (ANN) comprising:
a plurality of neurons arranged in layers with the outputs of one layer connected to the inputs of a plurality of the neurons of a subsequent layer, wherein neurons are connected to each other via a plurality of synapses, each of the synapses having a synaptic weight encoding a connection strength between two connected neurons, the magnitude of the weight of each of the synapses represented by a weighted current flow from multiple conductance pairs, each of the multiple conductance pairs representing a joint contribution and having a higher-significance conductance-pair and a lower-significance conductance-pair, wherein a plurality of training examples are serially input to the ANN while observing its output, wherein a backpropagation algorithm updates the synaptic weight in response to a difference between the output from a given layer and a desired output from the given layer, and wherein the ANN is configured to perform a method comprising:
(a) pausing training and measuring conductances across analog memory elements in the ANN, and computing an original synaptic weight for each synapse by arithmetic contributions from two or more conductance pairs;
(b) identifying at least one measured conductance in a given conductance pair whose absolute value is greater than its paired conductance by a predetermined amount;
(c) reconfiguring the lower-significance conductance pairs to be substantially equal, corresponding to a zero joint contribution towards an overall synaptic weight, and reconfiguring one of the more-significant conductance pairs until said original synaptic weight value is obtained; and
(d) inverting the polarity of the conductance-pair of lower significance for subsequent training operations.