Optichron, Inc.

Netlogic Microsystems, Inc

Cyber Atomics, Inc.

National Semiconductor Corporation

Broadcom Corporation

Other

Controlnet, Inc.

Avago Technologies International Sales Pte. Limited

Roy G Batruni

Howard Allen Wilson

Vinod K Bhardwaj

Franz C Chen

Karl Robert Griep

An efficient search includes: inputting data comprising a vector that requires a first amount of memory; compressing the vector into a compressed representation while preserving information content of the vector, including: encoding, using one or more non-quantum processors, at least a portion of the vector to implement a quantum gate matrix; and modulating a reference vector using the quantum gate matrix to generate the compressed representation; searching the compressed vector in a database; and outputting a search result to be displayed, stored, and/or further processed.

Quantum modulation-based data search

Data compression includes: inputting data comprising a vector that requires a first amount of memory; compressing the vector into a compressed representation while preserving information content of the vector, including: encoding, using one or more non-quantum processors, at least a portion of the vector to implement a quantum gate matrix; and modulating a reference vector using the quantum gate matrix to generate the compressed representation, wherein the compressed representation requires a second amount of memory that is less than the first amount of memory; and outputting the compressed representation to be displayed, stored, and/or further processed.

Quantum modulation-based data compression

A data processing technique includes: accessing a matrix (M) representing a graph; wherein: the graph comprises a plurality of nodes to be clustered and a plurality of edges; an edge in the plurality of edges represents an association between two of the plurality of nodes; and an entry of the matrix has a corresponding edge among the plurality of edges. The technique further includes performing an operation on the matrix to generate a result matrix, the operation includes a multiplication function on the matrix; and identifying one or more clusters among the plurality of nodes, based at least in part on the result matrix, including detecting one or more vertices among the plurality of nodes using the result matrix.

Efficient data clustering

Matrix processing includes: initializing a current matrix based at least in part on an original matrix; iteratively determining a matrix property using a plurality of iteration cycles, including, in an iteration cycle: partitioning the current matrix to obtain a plurality of partitions, wherein the plurality of partitions includes a submatrix; modifying the submatrix based at least in part on other partitions of the plurality of partitions to provide a current matrix for a next iteration; and continuing to iterate until a condition is met. Matrix processing further includes obtaining the matrix property from an iteration result; and outputting the matrix property.

Efficient matrix property determination with pipelining and parallelism

Tensor analysis includes: obtaining a set of Fourier domain singular value decomposition (SVD) factors of a first tensor representing data in multiple dimensions, including: performing Fourier transform on the first tensor along a first dimension to obtain a Fourier domain tensor; performing SVD on the Fourier domain tensor to obtain a first set of Fourier domain SVD tensors; performing inverse Fourier transform on at least some of the first set of Fourier domain SVD tensors to obtain a set of native domain tensors; performing Fourier transform on at least some of the set of native domain tensors to obtain a second set of Fourier domain tensors; performing SVD on at least some of the second set of Fourier domain tensors to obtain a second set of Fourier domain SVD tensors; obtaining an SVD result based at least in part on the set of Fourier domain SVD factors of the first tensor; and outputting at least a portion of the SVD result.

Single- and multi-variate tensor spectral analysis

Matrix processing includes: accessing an original matrix; iteratively determining a plurality of estimated singular vectors of the original matrix, a plurality of estimated singular values of the original matrix, or both, using a plurality of iteration cycles; wherein at least some of the plurality of iteration cycles are performed in parallel on a plurality of processors; and outputting the plurality of estimated singular vectors of the original matrix, the plurality of estimated singular values of the original matrix, or both.

Iterative and efficient technique for singular value decomposition

A tensor processing technique includes: accessing a first tensor representing interconnections of a plurality of nodes, the first tensor being a tensor of three or more dimensions; accessing a second tensor; convolving the first tensor with the second tensor to generate a convolution result tensor; and outputting at least a portion of the convolution result tensor.

Multidimensional connectivity graph-based tensor processing

A tensor processing technique includes: accessing a tensor, wherein the tensor: represents interconnections of nodes across one or more dimensions, comprises a plurality of matrices, and forms a plurality of vectors across at least one of the one or more dimensions; applying Fourier Transform on the tensor to obtain a plurality of harmonic matrices; performing singular value decompositions (SVDs) on the plurality of harmonic matrices to obtain a plurality of corresponding SVD results; reducing the plurality of corresponding SVD results, including selecting one or more dominant components in the plurality of corresponding SVD results to obtain one or more reduced results; and performing Inverse Fourier Transform on the one or more reduced results to obtain a de-noised tensor.

Multidimensional tensor processing

A system and method are disclosed for securely transmitting and receiving a signal. A nonlinear keying modulator is used in the transmitter to encrypt the signal using a nonlinear keying modulation technique. A nonlinear keying demodulator is used in the receiver to decrypt the signal.

Secure modulation and demodulation

A signal processing method includes inputting a digital signal, providing a plurality of coefficients; and determining an output. The output is approximately equal to an aggregate of a plurality of linear reference components, and each of the linear reference components is approximately equal to an aggregate of a corresponding set of digital signal samples that is scaled by the plurality of coefficients.

Low-power and low-cost adaptive self-linearization system with fast convergence

An adaptive distortion reduction system comprising: an input interface to receive a distorted signal comprising a distorted component and an undistorted component, the distorted component being at least in part attributed to an exogenous signal; and an adaptive distortion reduction module coupled to the input interface, to perform linearization based at least in part on the distorted signal and information associated with the exogenous signal, to obtain a corrected signal that is substantially similar to the undistorted component; wherein the adaptive self-linearization module includes: a first digital signal processor (DSP) that is adapted to obtain a filter transfer function that approximates a transfer function to be corrected; and a second DSP that is configured using configuration parameters of the first DSP.

Distortion cancellation using adaptive linearization

A digital signal processing system comprising: an input terminal to receive an input signal that includes a distorted component and an undistorted component, the input signal having a sampling rate of R; and an adaptive self-linearization module coupled to the input terminal, to perform self-linearization based at least in part on the input signal to obtain an output signal that is substantially undistorted, wherein: the adaptive self-linearization module is to generate a replica distortion signal that is substantially similar to the distorted component, the generation being based at least in part on a target component having a sampling rate of R/L, L being an integer greater than 1; the adaptive self-linearization module includes a first digital signal processor (DSP) that is adapted to obtain a filter transfer function that approximates a system distortion transfer function, and a second DSP that is configured using configuration parameters of the first DSP.

Growing community of inventors

Fremont, CA, United States of America

Roy G Batruni