International Business Machines Corporation

Egypt Nanotechnology Center

Alessandro Curioni

Konstantinos Bekas

Wanda Andreoni

Christoph Hermann Hagleitner

Raphael C Polig

Abu Sebastian

Heiner Giefers

Christoph M Angerer

Evangelos Stavros Eleftheriou

Silvio Dragone

Manuel Le Gallo-Bourdeau

Carlo A Pignedoli

Devendra K Sadana

Vijay Narayanan

Jeehwan Kim

Eduard Albert Cartier

Sufi Zafar

Rajarao Jammy

Paul Charles Jamison

Fenton Read McFeely

Alessandro Cesare Callegari

Christopher Peter D'Emic

Michael Abramovich Gribelyuk

Wabe W Koelmans

Dianne L Lacey

Adelmo Cristiano Innocenza Malossi

Federico Zipoli

Peter W J Staar

Yves G Ineichen

Petr Khomyakov

Costas Bekas

Evgeni Petrovich Gousev

Augustin J Hong

Joseph Francis Shepard, Jr

Tomas Tuma

Stefan Woerner

Ivano Tavernelli

Valery Weber

Ahmed Abou-Kandil

Panagiotis Barkoutsos

Teodoro Laino

Evengi Gousev

Joseph P Shepard, Jr

Stephen A Shevlin

Fotios Gkritsis

Nasser D Afify

Cristiano Malossi

Nasser Afify

Photovoltaic device with band-stop filter. The photovoltaic device includes an amorphous photovoltaic material and a band-stop filter structure having a stopband extending from a lower limiting angular frequency ω≥0 to an upper limiting angular frequency ωwhere ω>ω. The band-stop filter structure is arranged in the photovoltaic device relative to the photovoltaic material in order to attenuate electromagnetic radiations reaching the photovoltaic material with angular frequencies of ω* in the stopband, so that ω<ω*<ω. The angular frequencies ω* correspond to electronic excitations ℏω* from valence band tail (VBT) states of the amorphous photovoltaic material to conduction band tail (CBT) states of the amorphous photovoltaic material.

Photovoltaic device with band-stop filter

A method of designing a molecule for an environment of interest using a quantum computer includes providing a linear superposition of a plurality of molecular species, the plurality of molecular species being initially weighted by equal initial coefficients; determining a lowest-energy quantum state for the superposition of the plurality of molecular species in a vacuum environment and in the environment of interest using a quantum optimization process; calculating a difference in lowest energy states between the vacuum environment and the environment of interest for each molecular species to provide a cost of the superposition of the plurality of molecular species; performing a quantum optimization process to determine a minimum cost for the superposition of the plurality of molecular species and to determine updated coefficients weighting the plurality of molecular species; and identifying the molecule for the environment of interest based on a comparison of the updated coefficients.

System and method for molecular design on a quantum computer

According to some embodiments, a computer-implemented method for performing sparse matrix dense matrix (SpMM) multiplication on a single field programmable gate array (FPGA) module comprising a k-stage pipeline is described. The method may include interleaving k-stage threads on the k-stage pipeline comprising a plurality of threads tto t, wherein a first result of thread tis ready one cycle after the first input of thread tis fed into the pipeline, and outputting a result matrix Y.

Sparse matrix multiplication using a single field programmable gate array module

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 programming 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.

Higher accuracy of non-volatile memory-based vector multiplication

Embodiments of the present invention may provide the capability to evaluate logarithm and power (exponentiation) functions using either hardware specific instructions, or a hardware specific implementation with reduced memory requirements. An input comprising a floating point representation of a real number may be received and a mantissa and an exponent may be extracted. A function of a logarithm of a mantissa of the real number may be approximated by utilizing a polynomial based on the mantissa. The approximated function of the logarithm may be combined with the exponent for calculating a value comprising a logarithm of the real number. Likewise, an input comprising a floating point representation of a real number and a representation of a second number may be received and an approximation of the real number to the power of the second number may be generated.

Logarithm and power (exponentiation) computations using modern computer architectures

A computing system includes computational memory and digital combinational circuitry operatively coupled with the computational memory. The computational memory is configured to perform computations at a prescribed precision. The digital combinational circuitry is configured to increase the precision of the computations performed by the computational memory. The computational memory and the digital combinational circuitry may be configured to iteratively perform a computation to a predefined precision. The computational memory may include circuitry configured to perform analog computation using values stored in the computational memory, and the digital combinational circuitry may include a central processing unit, a graphics processing unit and/or application specific circuitry. The computational memory may include an array of resistive memory elements having resistance or conductance values stored therein, the respective resistance or conductance values being programmable.

Mixed-precision memcomputing system

Embodiments relate to simplifying large and complex networks and graphs using global connectivity information based on calculated node centralities. An aspect includes calculating node centralities of a graph until a designated number of central nodes are detected. A percentage of the central nodes are then selected as pivot nodes. The neighboring nodes to each of the pivot nodes are then collapsed until the graph shrinks to a predefined threshold of total nodes. Responsive to the number of total nodes reaching the predefined threshold, the simplified graph is outputted.

Simplification of large networks and graphs

Embodiments of the present invention provide methods, computer program products, and systems for solving a linear equation system using a hardware-implemented extended solver, wherein a calculation precision is adapted in each iteration step of a solving process is provided. Embodiments of the present invention can be used to perform on-the-fly interpolations using the data associated with the highest resolution of the three-dimensional finite element voxel model to a lower resolution than the highest resolution as well as to perform solving computations of the solving process in the lower resolution.

Linear FE system solver with dynamic multi-grip precision

Embodiments include performing sparse matrix-matrix multiplication. Aspects include receiving a first matrix and a second matrix, providing a pseudo-space for the first and second matrices, and defining pseudo-space segments and assigning the pseudo-space segments to certain processes. Aspects also include assigning matrix elements of the first and second matrix to pseudo-space segments using a midpoint method thereby assigning the matrix elements to processes associated with the pseudo-space segments, assigning a result matrix element of a result matrix to a pseudo-space segment using a midpoint method thereby assigning the result matrix element to a further process associated with the pseudo-space segment and transmitting matrix elements of the first and second matrix required to establish a result matrix element to the further process which processes the result matrix element. Aspects further include performing a multiplication procedure by the further process based on the received matrix elements of the first and second matrix.

Method for performing sparse matrix-matrix multiplication

A method of fabricating a resistive memory element having a layer structure includes: providing a substrate; depositing a first electrode on an upper surface of the substrate; forming a layer of confining material on an upper surface of the first electrode so as to define a cavity having a maximal lateral dimension that is less than 60 nm along a direction parallel to an average plane of the first electrode, the confining material having a thermal conductivity greater than 0.5 W/(m&#xb7;K); depositing a resistively switchable material as an amorphous compound comprising carbon to fill the cavity; and depositing a second electrode on an upper surface of the resistively switchable material.

Amorphous carbon resistive memory element with lateral heat dissipating structure

An iterative refinement apparatus configured to generate data defining a solution vector x for a linear system represented by Ax=b, where A is a predetermined matrix and b is a predetermined vector. An outer solver processes input data, defining the matrix A and vector b, in accordance with an outer loop of an iterative refinement method to generate said data defining the solution vector x. An inner solver processes data items in accordance with an inner loop of the iterative refinement method. The inner solver is configured to process said data items having variable bit-width and data format. A precision controller determines the bit-widths and data formats of the data items adaptively in dependence on the results of the processing steps of the iterative refinement method; the precision controller configured to control operation of the inner solver for processing said data items with the bit-widths and data formats.

Iterative refinement apparatus

A resistive memory element is provided having a layer structure. The layer structure includes two layers forming two electrically conductive electrodes, respectively, a resistively switchable material sandwiched between the two layers forming the two electrodes, and in electrical connection therewith, and a confining material. The resistively switchable material is laterally confined within the confining material, between the two layers forming the electrodes. The confining material is sufficiently electrically insulating for an electric signal applied between the two conductive electrodes to change a resistance state of the memory element in operation. The confining material has a thermal conductivity greater than 0.5 W/(m&#xb7;K), and preferably greater than or equal to 30 W/(m&#xb7;K). The resistively switchable material is an amorphous compound comprising carbon, which has a maximal lateral dimension, along a direction parallel to an average plane of the two layers forming the electrodes, that is less than 60 nm.

A conjugate gradient solver apparatus is provided for generating data defining a solution vector x for a linear system represented by Ax=b where A is a predetermined matrix and b is a predetermined vector. The apparatus includes solver circuitry and a precision controller. The solver circuitry processes input data, defining said matrix A and vector b, in accordance with an iterative conjugate gradient method to generate said data defining the solution vector x. The solver circuitry is adapted to process data items, corresponding to vectors used in said conjugate gradient method, having a variable fixed-point data format. The precision controller determines the fixed-point data formats of respective said data items adaptively during progress of the conjugate gradient method in the solver circuitry.

Conjugate gradient solvers for linear systems

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Gattikon, Switzerland

Alessandro Curioni