查看文章

Neuromorphic computing embraces the “device history” offered by many analog non-volatile memory (NVM) devices to implement the small weight changes computed by a gradient-descent learning algorithm such as backpropagation. Deterministic and stochastic imperfections in the conductance response of real NVM devices can be encapsulated for modeling within a pair of “jump-tables.” Such jump-tables describe the full cumulative distribution function of conductance-change at each device conductance value, for both weight potentiation (SET) and depression (RESET). First, using several types of artificially constructed jump-tables, we revisit the relative importance of deviations from an ideal NVM with perfectly linear conductance response. Then, using jump-tables measured on improved non-filamentary resistive RAM devices based on Pr _0.7 Ca _0.3 MnO ₃ [see companion paper], we simulate the effects of their …