Abstract: A memory device enabling a reduced minimal conductance state may be provided. The device comprises a first electrode, a second electrode and phase-change material between the first electrode and the second electrode, wherein the phase-change material enables a plurality of conductivity states depending on the ratio between a crystalline and an amorphous phase of the phase-change material. The memory device comprises additionally a projection layer portion in a region between the first electrode and the second electrode. Thereby, an area directly covered by the phase-change material in the amorphous phase in a reset state of the memory device is larger than an area of the projection layer portion oriented to the phase-change material, such that a discontinuity in the conductance states of the memory device is created and a reduced minimal conductance state of the memory device in a reset state is enabled.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for a gated recurrent neural network (RNN). The exemplary embodiments may include providing an element processor, providing a distinct memory array for a respective set of one or more elements of a hidden state vector, storing in the memory array a group of columns of weight matrices that enable a computation of the set of one or more elements, computing one or more elements of each of multiple activation vectors using a set of one or more columns of the group of columns associated with each of the multiple activation vectors, and performing by the element processor an elementwise gating operation on computed elements, resulting in the set of one or more elements.

Abstract: A projected memory device includes a carbon-based projection component. The device includes two electrodes, a memory segment, and a projection component. The projection component and the memory segment form a dual element that connects the two electrodes. The projection component extends parallel to and in contact with the memory segment. The memory segment includes a resistive memory material, while the projection component includes a thin film of non-insulating material that essentially comprises carbon. In a particular implementation, the non-insulating material and the projection component essentially comprises amorphous carbon. Using carbon and, in particular, amorphous carbon, as a main component of the projection component, allows unprecedented flexibility to be achieved when tuning the electrical resistance of the projection component.

Abstract: A projected memory device includes a carbon-based projection component. The device includes two electrodes, a memory segment, and a projection component. The projection component and the memory segment form a dual element that connects the two electrodes. The projection component extends parallel to and in contact with the memory segment. The memory segment includes a resistive memory material, while the projection component includes a thin film of non-insulating material that essentially comprises carbon. In a particular implementation, the non-insulating material and the projection component essentially comprises amorphous carbon. Using carbon and, in particular, amorphous carbon, as a main component of the projection component, allows unprecedented flexibility to be achieved when tuning the electrical resistance of the projection component.

Abstract: A projected memory device includes a carbon-based projection component. The device includes two electrodes, a memory segment, and a projection component. The projection component and the memory segment form a dual element that connects the two electrodes. The projection component extends parallel to and in contact with the memory segment. The memory segment includes a resistive memory material, while the projection component includes a thin film of non-insulating material that essentially comprises carbon. In a particular implementation, the non-insulating material and the projection component essentially comprises amorphous carbon. Using carbon and, in particular, amorphous carbon, as a main component of the projection component, allows unprecedented flexibility to be achieved when tuning the electrical resistance of the projection component.

Abstract: The present disclosure relates to a method for executing a computation task composed of at least one set of operations where subsets of pipelineable operations of the set of operations are determined in accordance with a pipelining scheme. A single routine may be created for enabling execution of the determined subsets of operations by a hardware accelerator. The routine has, as arguments, a value indicative of input data and values of configuration parameters of the computation task, where a call of the routine causes a scheduling of the subsets of operations on the hardware accelerator in accordance with the values of the configuration parameters. Upon receiving input data of the computation task, the routine may be called to cause the hardware accelerator to perform by the computation task in accordance with the scheduling.

Abstract: The invention is directed to a device for high-dimensional encoding of a plurality of sequences of quantitative data signals. The device comprises a plurality of input channels for receiving the plurality of sequences of quantitative data signals and an encoding unit. The encoding unit is configured to perform a temporal high-dimensional encoding of n-grams of the plurality of sequences of quantitative data signals; thereby creating a plurality of temporally encoded hypervectors for the plurality of input channels. The encoding unit is further configured to perform a spatial high-dimensional encoding of the plurality of temporally encoded hypervectors, thereby creating a temporally and spatially encoded hypervector. The device further comprises a configuration controller. The configuration controller is adapted to configure the high-dimensional encoding in dependence on one or more hyperparameter values.

Abstract: The invention is directed a device for high-dimensional encoding of a plurality of sequences of quantitative data signals. The device comprises a memory crossbar array comprising a plurality of resistive devices, a first peripheral circuit connected to the memory crossbar array, and a second peripheral circuit connected to the first peripheral circuit. The device is configured to receive the plurality of sequences of quantitative data signals via a plurality of input channels and to store elements of a plurality of precomputed basis hypervectors as conductance states of the resistive devices. The plurality of basis hypervectors are bound to respective input channels. The first peripheral circuit performs a temporal encoding of n-grams of the quantitative data signals thereby creating a plurality of temporally encoded hypervectors. The second peripheral circuit performs a spatial encoding of the plurality of temporally encoded hypervectors. This creates a temporally and spatially encoded hypervector.

Abstract: The invention is notably directed at a device for high-dimensional computing comprising an associative memory module. The associative memory module comprises one or more planar crossbar arrays. The one or more planar crossbar arrays comprise a plurality of resistive memory elements. The device is configured to program profile vector elements of profile hypervectors as conductance states of the resistive memory elements and to apply query vector elements of query hypervectors as read voltages to the one or more crossbar arrays. The device is further configured to perform a distance computation between the profile hypervectors and the query hypervectors by measuring output current signals of the one or more crossbar arrays. The invention further concerns a related method and a related computer program product.

Abstract: A co-processor for performing a matrix multiplication of an input matrix with a data matrix in one step may be provided. The co-processor receives input signals for the input matrix as optical signals. A plurality of photonic memory elements is arranged at crossing points of an optical waveguide crossbar array. The plurality of memory elements is configured to store values of the data matrix. Input signals are connected to input lines of the optical waveguide crossbar array. Output lines of the optical waveguide crossbar array represent a dot-product between a respective column of the optical waveguide crossbar array and the received input signals, and values of elements of the input matrix to be multiplied with the data matrix correspond to light intensities received at input lines of the respective photonic memory elements. Additionally, different wavelengths are used for each column of the input matrix optical signals.

Abstract: A computer-implemented method of mixed-precision deep learning with multi-memristive synapses may be provided. The method comprises representing, each synapse of an artificial neural network by a combination of a plurality of memristive devices, wherein each of the plurality of memristive devices of each of the synapses contributes to an overall synaptic weight with a related device significance, accumulating a weight gradient ?W for each synapse in a high-precision variable, and performing a weight update to one of the synapses using an arbitration scheme for selecting a respective memristive device, according to which a threshold value related to the high-precision variable for performing the weight update is set according to the device significance of the respective memristive device selected by the arbitration schema.

Abstract: 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.

Abstract: A system can include a memristive crossbar array, which can include row lines and column lines intersecting the row lines. Resistive memory elements can be coupled between the row lines and the column lines at the junctions formed by the row and column lines. The resistive memory elements represent the values of the matrix. The system can further include an analogue circuit. The system can be configured to perform an exponentiation of the values of the vector in accordance with a first exponent. The crossbar array can be configured to apply the resulting values of the vector to the resistive elements thereby generating currents. The analogue circuit can be configured to perform an exponentiation of the generated currents in accordance with a second exponent.

Abstract: Artificial neuron apparatus includes first and second resistive memory cells. The first resistive memory cell is connected in first circuitry having a first input and output. The second resistive memory cell is connected in second circuitry having a second input and output. The first and second circuitry are operable in alternating read and write phases to apply a programming current to their respective memory cells on receipt of excitatory and inhibitory neuron input signals, respectively. During the write phase, resistance of the respective cells is changed in response to successive excitatory and inhibitory neuron input signals. During the read phase, a read current is applied to their respective cells to produce first and second measurement signals, respectively. An output circuit connected to the first and second outputs produces a neuron output signal at a neuron output when a difference between the first and second measurement signals traverses a threshold.

Abstract: A method and system providing a multi-memristive synaptic element for a cognitive computing system. The multi-memristive synaptic element comprises an array of memristive devices. The method comprises arbitrating a synaptic weight allocation, a related synaptic weight being represented by a synaptic weight variable of said multi-memristive synaptic element, updating said synaptic weight variable by a delta amount, and assigning said memristive devices to elements of a clock-like ordered circular list for selecting a particular memristor of said memristive devices requiring to be updated by a deterministic, periodic global clock that points to a different memristor at every clock tick, such that said multi-memristive synaptic element has a larger dynamic range and a more linear conductance response than a single memristor synaptic element.

Abstract: The device provides a resistive memory device for storing elements of hyper-dimensional vectors, in particular digital hyper-dimensional, as conductive statuses in components in particular in 2D-memristors, of the resistive memory device, wherein the resistive memory device provides a first crossbar array of the components, wherein the components are memristive 2D components addressable by word-lines and bit-lines, and a peripheral circuit connected to the word-lines and bit-lines and adapted for encoding operations by activating the word-lines and bit-lines sequentially in a predefined manner.

Abstract: A method, computer system, and computer program product of performing a matrix convolution on a multidimensional input matrix for obtaining a multidimensional output matrix. The matrix convolution may include a set of dot product operations for obtaining all elements of the output matrix. Each dot product operation of the set of dot product operations may include an input submatrix of the input matrix and at least one convolution matrix. The method may include providing a memristive crossbar array configured to perform a vector matrix multiplication. A subset of the set of dot product operations may be computed by storing the convolution matrices of the subset of dot product operations in the crossbar array and inputting to the crossbar array one input vector comprising all distinct elements of the input submatrices of the subset.

Abstract: The present disclosure relates to a method for executing a computation task composed of at least one set of operations where subsets of pipelineable operations of the set of operations are determined in accordance with a pipelining scheme. A single routine may be created for enabling execution of the determined subsets of operations by a hardware accelerator. The routine has, as arguments, a value indicative of input data and values of configuration parameters of the computation task, where a call of the routine causes a scheduling of the subsets of operations on the hardware accelerator in accordance with the values of the configuration parameters. Upon receiving input data of the computation task, the routine may be called to cause the hardware accelerator to perform by the computation task in accordance with the scheduling.

Abstract: A device performs a matrix-vector multiplication of a matrix with a vector. The device includes a crossbar array having row lines, column lines and junctions arranged between the row lines and the column lines. Each junction includes a programmable resistive element and an access element for accessing the programmable resistive element. The device further includes a signal generator configured to apply programming signals to the resistive elements to program conductance values for the matrix-vector multiplication. The device further includes a readout circuit and control circuitry configured to control the signal generator and the readout circuit. The readout circuit is configured to apply read voltages having a positive voltage sign and negative read voltages having a negative voltage sign to the row lines of the crossbar array. The readout circuit is further configured to read out column currents of the plurality of column lines of the crossbar array.

Abstract: 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 said 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 said item.