System and method for constructing synaptic weights for artificial neural networks from signed analog conductance-pairs of varying significance

摘要

Artificial neural networks (ANNs) are a distributed computing model in which computation is accomplished with many simple processing units, called neurons, with data embodied by the connections between neurons, called synapses and by the strength of these connections, the synaptic weights. An attractive implementation of ANNs uses the conductance of non-volatile memory (NVM) elements to record the synaptic weight, with the important multiply—accumulate step performed in place, at the data. In this application, the non-idealities in the response of the NVM such as nonlinearity, saturation, stochasticity and asymmetry in response to programming pulses lead to reduced network performance compared to an ideal network implementation. A method is shown that improves performance by distributing the synaptic weight across multiple conductances of varying significance, implementing carry operations between less-significant signed analog conductance-pairs to more-significant analog conductance-pairs.