46 results about "Matrix compression" patented technology

The invention belongs to the technical field of GNSS-R SAR (Global Navigation Satellite System-Radar Synthetic Aperture Radar) super-resolution imaging, and discloses a fast distance super-resolution imaging method based on a GNSS-R SAR. According to the method, matrix compression, regularization modeling and a rapid optimization algorithm are creatively combined, and a set of efficient and stable distance super-resolution processing flow is formed. Specifically, after a preliminary imaging result of a GNSS echo signal is obtained and a signal convolution model form of the GNSS echo signal is given, firstly, dimension reduction processing is performed on an observation matrix in a signal convolution model through singular value decomposition, and on the basis of a weighted singular value maintenance strategy processing result, the signal convolution model is reconstructed by using an inverse matrix of a truncated measurement matrix; then, starting from a regularization strategy, introducing an L1 norm constraint to construct a target function by utilizing the sparse characteristic of a target; and finally, solving the target function by adopting a rapid iterative optimization algorithm. According to the method provided by the invention, the range resolution of the GNSS echo data is remarkably improved.

The invention relates to the technical field of fault diagnosis, in particular to a power grid fault analysis method and system, and the method comprises the steps: constructing a fusion tensor, and carrying out the dimension reduction of the fusion tensor, and generating a fusion feature matrix; constructing a convolutional auto-encoder model to detect an abnormal fusion feature matrix; and performing power grid fault classification according to abnormal data of the abnormal feature matrix, and calculating a power grid equipment fault area based on a power grid fault classification result. The method has the beneficial effects that the power grid operation data is complemented by adopting a high-precision low-rank tensor complementation algorithm, and the completeness and accuracy of the data are ensured. By using Tucker decomposition and matrix compression technologies, key features are extracted from multi-dimensional data collected by a multi-source sensor, and a fusion feature matrix is constructed, so that the data processing efficiency and the feature expression capability are effectively improved. The application of the convolutional auto-encoder model enhances the detection precision of abnormal features, and the method based on improved weighted summation and hierarchical clustering improves the accuracy of fault classification and positioning.

The invention relates to a krylov subspace dimensionality reduction electric power flow calculation solving system, and the system comprises a power balance module which is used for forming an n-dimensional linear power balance equation set based on a Jacobian matrix and a power unbalance matrix; the krylov subspace dimension reduction solving module is used for carrying out iterative calculation on the n-dimensional linear power balance equation set by utilizing the standard orthogonal basis after orthogonalization calculation to obtain a power correction approximate optimal solution in the m-dimensional subspace; and the double linked list matrix compression and storage module is used for being responsible for matrix operation during iterative calculation of the krylov subspace dimension reduction solving module, and the matrix operation is performed based on matrix partitioning. According to the method, the calculation speed of existing power flow calculation solution can be increased, and the calculation efficiency and the resource utilization rate are improved.

The present invention belongs to the technical field of GNSS-R SAR super-resolution imaging, and discloses a fast range super-resolution imaging method based on GNSS-R SAR. The present invention innovatively combines matrix compression, regularization modeling and fast optimization algorithm to form a set of efficient and stable range super-resolution processing procedures. Specifically, after obtaining the preliminary imaging results of the GNSS echo signal and giving its signal convolution model form, the observation matrix in the signal convolution model is first reduced in dimension by singular value decomposition, and on the basis of the processing results of the weighted singular value preservation strategy, the signal convolution model is reconstructed using the inverse matrix of the truncated measurement matrix; then, starting from the regularization strategy, the L1 norm constraint is introduced to construct the objective function using the sparse characteristics of the target; finally, a fast iterative optimization algorithm is used to solve the objective function. The method of the present invention significantly improves the range resolution of GNSS echo data.

The invention discloses a multi-satellite joint task planning method and system for remote sensing observation, and relates to the field of remote sensing satellite task planning, and the method comprises the steps: carrying out the time window slice discretization of obtained satellite task data according to a matrix compression coding method, and obtaining a compression matrix; traversing and scanning each element in the compression matrix, removing invalid observation windows according to the elements, and aggregating the remaining valid time windows by using the time slice decision matrix to obtain the startup state of each satellite in each valid coverage time slice; in multi-satellite joint task planning, grid point division is carried out on a target area, and the joint coverage rate of multiple satellites in an overlapped time window is calculated; performing explosion decision on the time slice decision matrix through a multi-target fireworks algorithm to obtain a multi-satellite collaborative observation planning scheme; the multi-satellite joint task planning method and system aim at solving the problems that remote sensing satellites are low in scheduling efficiency, uneven in resource utilization and complex in coverage rate calculation in a multi-task and multi-constraint scene.

The disclosed embodiments relate to matrix compression / decompression instructions. In one example, a processor includes fetch circuitry to fetch a compression instruction having a format with fields to specify positions of an opcode and a decompressed source matrix and a compressed destination matrix; the decoding circuit is used for decoding the extracted compression instruction; and execution circuitry to, in response to the decoded compression instruction, generate a compressed result according to a compression algorithm by compressing a specified decompressed source matrix via any of: compress non-zero-value elements together and store a matrix position of each non-zero-value element in a header, or using fewer bits to represent one or more elements and using a header to identify matrix elements represented by fewer bits; and storing the compressed result to the specified compressed destination matrix.

The invention discloses a tensor autoregression model coefficient estimation optimization system and method based on a sketch matrix, and relates to the technical field of multi-dimensional time series data processing. The system comprises a multi-modal data acquisition module, a self-adaptive sketch matrix construction module, a tensor autoregression model construction module, a multi-stage coefficient estimation optimization module and the like. The method comprises the following steps: preprocessing multi-source time sequence data to construct a tensor, generating sparse sketch matrix compressed data based on PARAFAC decomposition and differential sampling, constructing a tensor autoregression model containing a lag term, optimizing a coefficient by combining an L1-L2 mixed regular term and an ADMM algorithm, dynamically updating the model, and deploying the model. According to the method, the calculation complexity is reduced through the sketch matrix, the comprehensive efficiency is improved by more than or equal to 50%, data key information retention is guaranteed, the reconstruction error is less than or equal to 5%, the prediction precision is reduced by less than or equal to 10%, edge equipment is adapted, the method can be applied to scenes such as Internet of Things monitoring and financial analysis, the contradiction between efficiency and precision in high-dimensional data processing is solved, and the operation and maintenance cost is reduced.

Disclosed are systems and techniques for performing matrix multiply operations on an expressive sparse matrix representation with limited metadata. The techniques include receiving a sparse matrix, metadata corresponding to the sparse matrix, and a matrix operand. The sparse matrix contains a first number (N) of elements to retain from a dense matrix which comprises at least a second number (M) of elements. The metadata corresponding to the sparse matrix is based on a third number (P) of positions and a format determined during compression of the dense matrix. The techniques include selecting, by one or more selection circuits, a subset of elements of the matrix operand based on the metadata corresponding to the sparse matrix and performing one or more matrix multiply operations on the sparse matrix and the subset of elements of the matrix operand.

The embodiment of the invention provides a data processing method and device, electronic equipment and a storage medium. The data processing method comprises the following steps: acquiring a first object matrix and a second object matrix which are used as matrix multiplication multipliers; compressing the first object matrix into a first target matrix and compressing the second object matrix into a second target matrix according to the first compression instruction; and multiplying the first target matrix and the second target matrix according to the first matrix multiplication operation instruction to obtain a first operation result matrix, and obtaining a matrix multiplication operation result of the first object matrix and the second object matrix according to the first operation result matrix. According to the method, the matrix compression and the matrix operation can be separated and executed in parallel, so that the flexibility of the matrix compression is improved, the design of a matrix operation instruction is simplified, and the matrix operation time is greatly shortened.

This invention discloses a parallelizable adaptive lossless matrix compression method. It involves dividing a large first matrix into smaller second matrices, assigning a splitting process to each second matrix, and having these splitting processes divide all second matrices into multiple bit matrices in parallel. Compression threads are then assigned to each bit matrix, and these compression threads adaptively select the optimal compression strategy from a set of compression strategies. Finally, the compression threads compress all bit matrices in parallel according to the determined compression strategy, forming a compressed matrix and a flag array as the final compressed data of the first matrix. By hierarchically and parallelizing the compression process, the memory overhead of the compression process is effectively reduced and the compression speed is improved. Adaptive compression is achieved by selecting the optimal compression strategy through trial compression, effectively improving the data compression ratio. Lossless data compression is achieved by constructing a flag array.

The invention discloses an MIMO wireless communication channel matrix compression calculation method and device, and the method comprises the steps: marking the position of a non-zero element in a channel matrix as 1, marking the position of a zero element as 0, and generating a binary index matrix; performing addressing and aggregation operation on non-zero elements in the channel matrix according to a column priority based on the binary index matrix, and sequentially writing the non-zero elements into a preset continuous storage space in the memory according to the column priority to generate a compression matrix; and sequentially aiming at each calculation unit, firstly determining a corresponding index column according to the binary index matrix, counting non-zero elements in the index column, if the count is greater than 0, performing multiplication and addition operation based on a multiplication and addition operation module, and if the count is equal to 0, writing a 0 value into a partial sum accumulation register as an operation result of the current calculation unit. According to the scheme of the invention, the storage overhead of the MIMO wireless communication channel matrix can be reduced, and the calculation efficiency of the channel matrix can be improved.

The present application provides a data compression method and a compression apparatus. Before executing matrix compression processing, the compression apparatus can process original data into matrix data suitable for a matrix compression algorithm, so that the original data can be used as an input of the matrix compression algorithm, thereby avoiding the problem of impossible use of the matrix compression algorithm due to irregular original data, and facilitating improvement of the data compression efficiency. Additionally, the present application further provides a data decompression method and a decompression apparatus. The decompression apparatus can acquire the compression apparatus and first information, and recover compressed data into the original data on the basis of the first information. Even when the compression apparatus executes preprocessing on the original data, the decompression apparatus can quickly and efficiently recover the compressed data into the original data on the basis of the first information, thereby facilitating improvement of the data decompression efficiency.

The present application relates to the technical field of fault diagnosis, in particular to a power grid fault analysis method and system, which comprises constructing a fusion tensor and performing dimension reduction processing on the fusion tensor to generate a fusion feature matrix; constructing a convolutional autoencoder model to detect abnormal fusion feature matrix; classifying power grid faults according to abnormal data of the abnormal feature matrix, and calculating power grid equipment fault area based on the result of power grid fault classification. The present application has the beneficial effect of ensuring the integrity and accuracy of data by using a high-precision low-rank tensor completion algorithm to complete power grid operation data. Key features are extracted from multi-dimensional data collected by multiple source sensors and a fusion feature matrix is constructed using Tucker decomposition and matrix compression technology, effectively improving the efficiency of data processing and the ability of feature expression. The application of the convolutional autoencoder model enhances the detection accuracy of abnormal features, and the improved weighted summation and hierarchical clustering-based method improves the accuracy of fault classification and positioning.

A system and method for performing multiplication for a neural network, e.g. for data of one or more layers in a neural network, may include loading a portion of a compressed version of a sparse input matrix into a cache memory; uncompressing a subset of the data in the portion of the compressed version of the sparse input matrix; and multiplying a sparse kernel matrix by the subset of the data using a set of instructions which are themselves created based on the sparse kernel matrix.

The invention discloses a multi-mode fretting wear calculation method, device and system and a storage medium. The multi-mode fretting wear calculation method comprises the steps that S1, dynamic node data is obtained, and the wear amount is calculated; s2, calculating a geometric self-adaptive wear direction; s3, performing node classification and index optimization and adaptive adjustment of an increment step; and S4, carrying out million-level node parallel computing by adopting a block storage and matrix compression technology, and controlling the grid scanning times through KMESHSWEEP parameters. By adopting the technical scheme of the invention, the technical problems of poor geometric adaptability, low algorithm convergence and limited calculation scale in fretting wear simulation are solved.

The invention discloses a key value compression method in a self-attention mechanism, a large language model and electronic equipment, and relates to the technical field of computers. The compression method comprises the following steps: respectively carrying out multiple times of residual decomposition on a key matrix and a value matrix to obtain a key residual vector and a value residual vector after each time of decomposition; respectively carrying out clustering compression on the key residual vector and the value residual vector after each decomposition, and carrying out attention calculation on the query matrix, the compressed key residual vector and the compressed value residual vector; and accumulating all attention calculation results. According to the method and the device, the problems that standard Softmax Transform parameters cannot be used by the Linear Transform, and the difference between the standard Softmax Transform parameters and the standard Softmax Transform parameters is large are solved.

The invention discloses a fully expressive sparse matrix representation with finite element data. Systems and techniques for compressing a dense matrix into a fully expressive sparse matrix representation with finite element data are disclosed. The techniques include generating a sparse matrix with corresponding metadata based on the dense matrix. Generating a sparse matrix with respective metadata includes: identifying a first number (M) of elements to be compressed, a second number (N) of elements to be retained, and a third number (B) of bits used by each metadata value; determining a metadata value of each of the N elements of the dense matrix; packing the first metadata value having more than B bits into a second metadata value having B bits; and generating a sparse matrix containing the N elements of the dense matrix. The techniques include storing a sparse matrix and corresponding metadata, wherein the corresponding metadata includes a second metadata value.

The invention discloses a hierarchical TCAM (Ternary Content Addressable Memory) message parsing and field extraction system based on parsing graph optimization, which relates to the technical field of communication, and comprises a software calculation configuration module used for carrying out graph topology optimization on a protocol parsing graph to generate a plurality of mutually independent parsing sub-graphs; and according to the key bit set corresponding to each analysis sub-graph, generating a hardware resource configuration table corresponding to each analysis sub-graph, and distributing the hardware resource configuration table to a key bit extraction unit, a mapping table matching unit, a flow classification module and a field extraction module on a hardware side, thereby configuring a logic structure of a target circuit according to hardware configuration information. And dynamic reconstruction of a protocol analysis function is realized. According to the method, the protocol analysis diagram is decomposed into the analysis sub-diagrams, the multi-level unit fusion architecture is adopted in the hardware level, streamlined protocol analysis is achieved, the TCAM storage requirement can be remarkably reduced in combination with the key bit matrix compression technology, and the time sequence and storage bottleneck during large-scale message header analysis is effectively solved.

The invention relates to a method for testing interface shear strength of an optical fiber embedded composite material based on an annular pressure head, and belongs to the technical field of interface performance testing of optical fibers and composite materials. Comprising the following steps: providing a group A sample and a group B sample of which the cross sectional areas are smaller than the area of a test pressure head; the A group of samples are composite material samples embedded with optical fibers, the end surfaces of the optical fibers and the surface of the matrix are coplanar, and the B group of samples are composite material samples not embedded with optical fibers; acquiring annular pressure data; obtaining matrix data; calculating shear strength; correcting the matrix compression load based on the ratio of the effective compression area of the A group of samples to the effective compression area of the B group of samples to obtain a matrix equivalent compression load; and calculating the difference between the total load and the equivalent compression load of the matrix to obtain an interface shear force, and further calculating the interface shear strength. The interference of the plastic deformation of the matrix on a test result is eliminated, and the accuracy and the reliability of a calculation result are remarkably improved.

The invention provides a high-order sparse matrix LDL efficient decomposition calculation acceleration method, which can be used for a high-order sparse matrix linear equation set solving calculation scene. The accelerator is constructed based on an FPGA platform, and efficient decomposition of a high-order sparse matrix is achieved through a software and hardware cooperative computing mechanism. The software level realizes symbolic analysis of a sparse positive definite matrix, calculation sequence dependency modeling and LDL decomposition hardware customization instruction generation; and the hardware acceleration unit realizes streamlined execution of decomposition tasks by performing instruction fetching, decoding and execution of customized instructions, and completes high-order matrix numerical calculation. The system comprises an instruction generation unit, an instruction analysis unit, a calculation module, a data access module and a control scheduling module, and all the modules work cooperatively to improve the degree of parallelism and the storage access efficiency. Matrix data types support fp16, fp32 and fp64, matrix compression is realized by adopting a sparse column (CSC) format and a matrix rearrangement method, and storage overhead and memory access delay are remarkably reduced while high calculation precision is ensured. Experiments show that compared with traditional CPU and GPU platforms, the sparse matrix solution performance and energy efficiency of the method are remarkably improved by one order of magnitude, and the method is suitable for high-precision engineering calculation and edge real-time solution scenes such as engineering simulation, structural mechanics, electromagnetic analysis and attitude calculation.

The invention relates to a weight matrix compression method, device and equipment for a high-computing-power chip and a storage medium product. The method comprises the following steps: performing bit slicing processing on an original weight matrix to obtain at least two bit slicing matrixes; classifying the at least two bit slice matrixes to obtain at least one first bit slice matrix and at least one second bit slice matrix; performing coding processing on the at least one second bit slice matrix to obtain at least one coded second bit slice matrix; determining a compressed weight matrix according to the at least one first bit slice matrix and the at least one coded second bit slice matrix; the sparse rate of the second bit slice matrix is greater than the sparse rate of the first bit slice; and the compressed weight matrix is applied to the model training of the Transform network. By adopting the method, the weight matrix storage load can be reduced, so that the memory access speed is accelerated, and the overall performance of the system is improved.

The present application discloses a key-value compression method, a large language model, and an electronic device in a self-attention mechanism, and relates to the field of computer technology. The compression method includes performing multiple residual decompositions on the key matrix and the value matrix, respectively, to obtain a key residual vector and a value residual vector after each decomposition; clustering and compressing the key residual vector and the value residual vector after each decomposition, and performing attention calculation on the query matrix, the compressed key residual vector, and the value residual vector; and accumulating all attention calculation results. The present application solves the problem that the Linear Transformer cannot use the standard Softmax Transformer parameters and has significant differences from the standard Softmax Transformer.

The invention provides an efficient solution calculation acceleration method for a high-order sparse matrix linear equation set. The high-order sparse matrix linear equation set is widely applied to the fields of scientific calculation, engineering simulation and the like. A traditional FPGA-based solving method is low in hardware resource utilization rate and complex in data flow scheduling, so that the calculation efficiency is limited, and the efficient solving requirement of a large-scale sparse matrix is difficult to meet. In order to solve the problem, the invention provides a high-order sparse matrix-oriented linear equation set efficient solution calculation acceleration method, a sparse matrix compression storage format is combined, a linear equation set solution special circuit and an acceleration solution algorithm based on LDL decomposition are designed, and the calculation efficiency is effectively improved.

Systems and techniques are described herein for compressing data used in cryptographic operations. For example, a process may include obtaining a first data structure, wherein the first data structure comprises polynomials; generating a second data structure based on the first data structure, wherein the second data structure comprises coefficients of the polynomials; sorting the second data structure in an ascending order to obtain a sorted second data structure; updating the sorted second data structure based on differences between elements of the sorted second data structure to obtain a delta-encoded data structure; performing an entropy coding on the delta-encoded data structure to obtain an entropy-encoded output; recovering an updated first data structure using the entropy-encoded output, wherein the updated first data structure corresponds to the first data structure with a different order of first data structure elements; and performing a cryptographic operation using the updated first data structure.

The utility model relates to the technical field of compression molding device, and disclose a kind of matrix compression molding device, and the anti-skid bottom plate is fixedly sleeved in the position around the surface of fixed bottom plate, first cross plate is movably connected in the position of both sides of fixed bottom plate top, second cross plate is movably connected in the position of both sides of first cross plate on fixed bottom plate top, second screw rod is movably connected in the position of the bottom of first cross plate in fixed bottom plate interior, first screw rod is movably connected in the position of the bottom of second cross plate in fixed bottom plate interior, servo motor is drivingly connected in the position of both ends of second screw rod and first screw rod, the utility model is equipped with servo motor, first screw rod, second screw rod, first cross plate, second cross plate, is favorable to after matrix compression molding, by servo motor drive first screw rod, second screw rod is rotated, and then make first cross plate, second cross plate separate to around, and then quickly the matrix of compression molding is demoulded, increase the production efficiency of matrix.

Embodiments herein relate to a neural network compression technique in which a weight matrix within a neural network is transformed via matrix multiplication with an orthogonal matrix. The orthogonal matrix is derived from a calibration data set, which is generally selected to widely represent expected runtime input data, and the transformation causes the resulting modified weight matrix to have components ordered by relative importance. The modified weight matrix is incorporated into a compressed neural network with fewer weights. By removing one or more components of lower importance, the size of the neural network (and thus its storage and execution overhead) is reduced while still maintaining acceptable performance levels.

The present invention provides a method and device for compressed storage of a finite element assembly matrix for process simulation. The method for compressed storage of a finite element assembly matrix for process simulation includes: using two arrays to store the positions and values of the elements of the matrix to be stored respectively. By using two arrays to store the positions and values of the elements of the matrix to be stored respectively, this method requires less space compared to directly storing the matrix to be stored, can greatly reduce the storage overhead required for the matrix to be stored, improve the memory usage efficiency in the numerical calculation process of the corresponding process simulation, and enhance the ability of the corresponding process simulation software to handle large-scale meshes. Moreover, this method has simple steps, is easy to implement, the operation process is transparent to users, does not affect the accuracy of the numerical calculation method for process simulation, does not significantly affect the efficiency of the numerical calculation in the process simulation process, improves the memory space usage efficiency, enhances the ability of the software to handle large-scale meshes, and has broad application prospects.

Systems and methods for decompressing a meaningful sparse matrix representation with limited metadata are disclosed. The methods include receiving a sparse matrix and metadata corresponding to the sparse matrix. The sparse matrix is ​​a compressed representation of a dense matrix. The sparse matrix contains a first number N of elements to be retained from the dense matrix, which comprises at least a second number M of elements. The metadata corresponding to the sparse matrix is ​​based on a third number P of positions and a format determined during the compression of the dense matrix. The methods include generating an uncompressed matrix based on the sparse matrix and the metadata corresponding to the sparse matrix.

The present invention relates to a Krylov subspace dimension reduction power flow calculation and solution system, comprising: a power balance module, which is used to form an n-dimensional linear power balance equation group based on a Jacobian matrix and a power imbalance matrix; a Krylov subspace dimension reduction solution module, which is used to iteratively calculate the n-dimensional linear power balance equation group using a standard orthogonal basis after orthogonalization calculation to obtain an approximate optimal solution for the power correction amount in an m-dimensional subspace; a bidirectional linked list matrix compression storage module, which is used to be responsible for the matrix operation during the iterative calculation of the Krylov subspace dimension reduction solution module, and the matrix operation is operated based on matrix block division. The present invention can speed up the operation speed of the existing power flow calculation and solution, and improve the calculation efficiency and resource utilization.

Provided is a new coupling matrix compression device for compressing an input coupling matrix corresponding to an input Ising Hamiltonian in an optimal solution search device that compresses the input coupling matrix, solves the input coupling matrix with an Ising machine using a coupling matrix obtained through compression, and restores a solution corresponding to the input coupling matrix from an output solution of the Ising machine. A coupling matrix compression device according to the disclosed technology comprises a compression determination unit. The compression determination unit reduces the comparison between the value of an Ising Hamiltonian in cases where the spins of the spin pair of interest have the same sign and the value of the Ising Hamiltonian in cases where the spins of the spin pair of interest have different signs to a number division problem and carries out evaluation, thereby determining the possibility of compression of the spin pair of interest, where, among spins coupled by an input coupling matrix or by a combined matrix after compression, the spin pair of interest is a spin pair to be subjected to compression determination, and peripheral spins are spins excluding the spin pair of interest.