International Business Machines Corporation

Vinay Manikrao Joshi

Abu Sebastian

Manuel Le Gallo-Bourdeau

Irem Boybat Kara

Christophe Piveteau

Simon Haefeli

A method of operating a neuromorphic system is provided. The method includes applying voltage signals across input lines of a crossbar array structure, the crossbar array structure including rows and columns interconnected at junctions via programmable electronic devices, the rows including the input lines for applying voltage signals across the electronic devices and the columns including output lines for outputting currents. The method also includes correcting, via a correction unit connected to the output lines, each of the output currents obtained at the output lines according to an affine transformation to compensate for temporal conductance variations in the electronic devices.

Conductance drift corrections in neuromorphic systems based on crossbar array structures

The present disclosure relates to an electronic system for computing items of an outer product matrix, for each item of at least part of the items of the matrix. The system is configured to receive a pair of real numbers of two vectors, the pair corresponding to the item. The system is further configured to compute a stochastic representation of the real numbers resulting in two sets of bits, the set of bits comprising a subset of bits representing the real number and a sign bit indicative of the sign of the real number. The system is further configured to perform a sequence of digital operations using the two sets of bits to provide a representation of the item.

Electronic system for computing items of an outer product matrix

Methods are provided for training weights of an artificial neural network to be implemented by inference computing apparatus in which the trained weights are stored as programmed conductance states of respective predetermined memristive devices. Such a method includes deriving for the memristive devices a probability distribution indicating distribution of conductance errors for the devices in the programmed conductance states. The method further comprises, in a digital computing apparatus: training the weights via an iterative training process in which the weights are repeatedly updated in response to processing by the network of training data which is propagated over the network via the weights; and applying noise dependent on said probability distribution to weights used in the iterative training process.

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Rueschlikon, Switzerland

Vinay Manikrao Joshi