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

Claim 8:
9. A method to train an artificial neural network (ANN), the 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, where 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, where a plurality of training examples are serially input to the ANN while observing its output, where 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, the method comprising:
(a) pausing training and measuring conductances across analog memory elements in the ANN;
(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 a similar synaptic weight value is obtained;
(d) inverting the polarity of the conductance-pair of lower significance for subsequent training operations, and
wherein a subset of the synaptic weights in the ANN is implemented using a capacitor tied to the gate of a read transistor, together with programming transistors for adding charge to, and subtracting charge from, the capacitor according to signals associated with the downstream and upstream neurons.