Abstract: A matrix-vector multiplication device includes a memory crossbar array with row lines, column lines, and junctions. Each junction comprises a programmable resistive element and an access element. A signal generator is configured to apply programming signals to the resistive elements to program conductance values for the matrix-vector multiplication and a readout circuit is configured to apply read voltages to the row lines and to read out current values of the column lines. Control circuitry is configured to control the signal generator and the readout circuit and to select, via the access terminals, a plurality of resistive elements in parallel according to a predefined selection scheme which applies the signals and/or the read voltages in parallel to resistive elements which do not share the same row and column line and applies the programming signals and/or the read voltages to at most one resistive element per row line and column line.

Abstract: A computer-implemented method for managing datasets of a storage system is provided, wherein the datasets have respective sets of metadata, the method including: successively feeding first sets of metadata to a spiking neural network (SNN), the first sets of metadata fed corresponding to datasets of the storage system that are labeled with respect to classes they belong to, so as to be associated with class labels, for the SNN to learn representations of said classes in terms of connection weights that weight the metadata fed; successively feeding second sets of metadata to the SNN, the second sets of metadata corresponding to unlabeled datasets of the storage system, for the SNN to infer class labels for the unlabeled datasets, based on the second sets of metadata fed and the representations learned; and managing datasets in the storage system, based on class labels of the datasets, these including the inferred class labels.

Abstract: A sensor arrangement for position sensing comprises a magnetic field source and a magnetoresistive element arranged in a magnetic field generated by the magnetic field source, which magnetoresistive element provides an output signal (R) dependent on a position (x) of the magnetoresistive element relative to the magnetic field source. A feedback controller is configured to receive the output signal (R) of the magnetoresistive element and is configured to adjust one or more of the position (x) of the magnetoresistive element relative to the magnetic field source and a strength of the magnetic field generated by the magnetic field source acting on the magnetoresistive element dependent on the output signal (R) of the magnetoresistive element.

Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.

Abstract: A sensor device comprising a computational memory and electronic circuitry. The sensor device is configured to receive an input signal, to compress the input signal into a compressed signal and to compute a reconstructed signal from the compressed signal. The electronic circuitry is configured to perform a reconstruction algorithm to compute the reconstructed signal. The computational memory is configured to compute the compressed signal and partial results of the reconstruction algorithm. A related method and a related design structure may be provided.

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 sensor device comprising a computational memory and electronic circuitry. The sensor device is configured to receive an input signal, to compress the input signal into a compressed signal and to compute a reconstructed signal from the compressed signal. The electronic circuitry is configured to perform a reconstruction algorithm to compute the reconstructed signal. The computational memory is configured to compute the compressed signal and partial results of the reconstruction algorithm. A related method and a related design structure may be provided.

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: Apparatus including: memory cell unit(s) having a variable-resistance channel component (CC) extending between first and second supply terminals for supplying read and write (R/W) signals to the unit in respective R/W modes, and resistive memory elements (RMEs) arranged along the CC, RME includes resistive memory material (RMM), extending along a respective channel segment (CHS) of the CC in contact therewith, in which respective lengths along that CHS of high- and low-resistance regions is variable in write mode, and a gate terminal provided on that CHS for controlling resistance of the CHS in response to control signal(s) (CS) applied to the gate terminal; and circuitry configured to apply the CS such that, in read mode, a RME(s) is selected by applying a CS producing CHS with resistance between the resistance regions of the RMM; and remaining RME(s) are deselected by applying CS producing CHS having resistance less than the low-resistance region.

Abstract: The invention relates to a method for hardware-implemented training of a feedforward artificial neural network. The method comprises: generating a first output signal by processing an input signal with the network, wherein a cost quantity to assumes a first cost value; measuring the first cost value; defining a group of at least one synaptic weight of the network for variation; varying each weight of the group by a predefined weight difference; after the variation, generating a second output signal from the input signal to measure a second cost value; comparing the first and second cost values; and determining, based on the comparison, a desired weight change for each weight of the group such that the cost function does not increase if the respective desired weight changes are added to the weights of the group. The desired weight change is based on the weight difference times ?1, 0, or +1.

Abstract: A sensor arrangement for position sensing comprises a row of multiple magnetoresistive elements. A magnetic field source (3) provides a magnetic field with a first magnetic pole (N) and a second magnetic pole (S). The magnetic field source (3) is arranged such that magnetoresistive elements of the row face one of: the first magnetic pole (N) or second magnetic pole (S). The first magnetoresistive element is arranged in the magnetic field and provides a first output signal dependent on a position of the magnetoresistive element relative to the magnetic field source (3). A measurement unit is configured to determine a position of the magnetic field source (3) relative to the magnetoresistive elements of the row dependent on the first output signals of the magnetoresistive elements.

Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: A device for performing a multiplication of a matrix with a vector. The device comprises a plurality of memory elements, a signal generator and a readout circuit. The signal generator is configured to apply programming signals to the memory elements. The signal generator is further configured to control a first signal parameter of the programming signals in dependence on matrix elements of the matrix and to control a second signal parameter of the programming signals in dependence on vector elements of the vector. The readout circuit is configured to read out memory values of the memory elements. The memory values represent result values of vector elements of a product vector of the multiplication. The memory elements may be in particular resistive memory elements or photonic memory elements. Additionally there is provided a related method and design structure for performing the multiplication of a matrix with a vector.

Abstract: A multiplication device for performing a matrix-vector-multiplication may be provided. The multiplication device comprises a memristive crossbar array comprising a plurality of memristive devices. The device comprises a decomposition unit adapted for decomposing a matrix into a partial sum of multiple sub-matrices, and decomposing a vector into a sum of multiple sub-vectors, a programming unit adapted for programing the plurality of the memristive devices with values representing elements of the sub-matrices such that each one of the memristive devices corresponds to one of the elements of the sub-matrices, an applying unit adapted for applying elements of one of the multiple sub-vectors as input values to the memristive crossbar array to input lines of the memristive crossbar array resulting in partial results at output lines of the memristive crossbar array, and a summing unit adapted for scaling and summing the partial results building the product of the matrix and the vector.

Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.

Abstract: A device for performing a multiplication of a matrix with a vector. The device comprises a plurality of memory elements, a signal generator and a readout circuit. The signal generator is configured to apply programming signals to the memory elements. The signal generator is further configured to control a first signal parameter of the programming signals in dependence on matrix elements of the matrix and to control a second signal parameter of the programming signals in dependence on vector elements of the vector. The readout circuit is configured to read out memory values of the memory elements. The memory values represent result values of vector elements of a product vector of the multiplication. The memory elements may be in particular resistive memory elements or photonic memory elements. Additionally there is provided a related method and design structure for performing the multiplication of a matrix with a vector.

Abstract: The invention is directed to a resistive memory device comprising a control unit for controlling a memory cell of the memory device. The memory cell includes a first terminal, a second terminal and a phase change segment comprising a phase-change material. The phase change segment is arranged between the first terminal and the second terminal. The phase change material is antimony. The phase change segment retains an amorphous region during a write operation. The control unit, during the write operation, applies an electrical programming pulse to the terminals to cause a portion of the phase change segment to transition from a crystalline phase to an amorphous phase comprising the amorphous region. A trailing edge duration of the electrical programming pulse is adjusted based on ambient temperature to prevent re-crystallization of the amorphous region. Shorter trailing edge durations are used at increasing ambient temperatures.

Abstract: The invention is notably directed to a resistive memory device comprising a control unit for controlling the resistive memory device and a plurality of memory cells. The plurality of memory cells includes a first terminal, a second terminal and a phase change segment comprising a phase-change material for storing information in a plurality of resistance states. The phase change segment is arranged between the first terminal and the second terminal. The phase change material consists of antimony. Furthermore, at least one of the dimensions of the phase change segment is smaller than 15 nanometers. Additional implementations of the resistive memory device include a related method, a related control unit, a related memory cell and a related computer program product.

Abstract: A neuromorphic synapse with a resistive memory cell connected in circuitry having first and second input terminals. The input terminals respectively receive pre-neuron and post-neuron action signals, each having a read portion and a write portion, in use. The circuitry includes an output terminal for providing a synaptic output signal which is dependent on resistance of the memory cell. The circuitry is configured such that the synaptic output signal is provided at the output terminal in response to application at the first input terminal of the read portion of the pre-neuron action signal, and such that a programming signal, for programming resistance of the memory cell, is applied to the cell in response to simultaneous application of the write portions of the pre-neuron and post-neuron action signals at the first and second input terminals respectively. The synapse can be adapted for operation with identical pre-neuron and post-neuron action signals.

Abstract: Methods and apparatus are provided for training an artificial neural network having a succession of layers of neurons interposed with layers of synapses. A set of crossbar arrays of memristive devices, connected between row and column lines, implements the layers of synapses. Each memristive device stores a weight for a synapse interconnecting a respective pair of neurons in successive neuron layers. The training method includes performing forward propagation, backpropagation and weight-update operations of an iterative training scheme by applying input signals, associated with respective neurons, to row or column lines of the set of arrays to obtain output signals on the other of the row or column lines, and storing digital signal values corresponding to the input and output signals. The weight-update operation is performed by calculating digital weight-correction values for respective memristive devices, and applying programming signals to those devices to update the stored weights.