35 results about "Numerical precision" patented technology

The invention discloses a split type three-box girder nonlinear vortex vibration steady-state amplitude intelligent prediction method, belongs to the field of bridge vortex-induced vibration control, and aims to solve the problem of low accuracy of three-box girder vortex vibration nonlinear modeling and amplitude prediction. The method comprises the following steps: acquiring displacement time sequence data through a reduced scale model wind tunnel test, preprocessing to obtain displacement, speed and acceleration dimensionless data, and dividing a data set; constructing a candidate function library containing high-order polynomial terms of the primary function; performing nonlinear system sparse recognition by taking acceleration data as a target item and combining an improved algorithm to obtain a preliminary feature set; a final feature set is obtained through energy-statistics-self-adaption three-stage screening; and finally, constructing a control equation to predict the steady-state amplitude. The method can accurately predict amplitude, coincide test and real bridge observation results, and is less in computing resource occupation and high in numerical value precision.

The invention relates to the technical field of data storage, and discloses a numerical data variable-length coding storage method, device, equipment and medium, which comprises the following steps: converting target numerical data into fixed-length binary representation according to definition precision, executing filling removal interception processing on an integer part, executing digital-level packet compression coding on a decimal part, and performing variable-length coding on the target numerical data; and generating a variable-length binary sequence containing length information and symbol information, and writing the sequence into a storage medium to realize compact storage of numerical data. According to the method, redundant filling bytes are eliminated from an integer part, compressed encoding is performed on a decimal part according to effective digits, and unified processing of sign bits is combined, so that a storage space dynamically changes along with actual numerical digits on the premise of keeping numerical precision and analyzability, space waste of fixed-length numerical types is reduced, and the method is suitable for large-scale popularization and application. Meanwhile, the precision error of the floating-point number is avoided, and the storage efficiency and the data reliability are improved.

The invention relates to a transient electromagnetic three-dimensional inversion method and device, computer equipment and a storage medium. The method comprises the following steps: decoupling a forward modeling grid and an inversion grid, independently generating a vertical self-adaptive layered special forward modeling grid for each time channel according to the skin depth of the time channel, and remarkably reducing the calculation dimension while keeping the horizontal discrete consistency; an equivalent circuit method is innovatively adopted to achieve high-precision mapping of model parameters from an inversion space to a forward modeling space, a plurality of inversion grid units arranged in the current magnetic field component direction are regarded as series connection, and a plurality of inversion grid units arranged in the direction perpendicular to the current magnetic field component direction are regarded as parallel connection. The equivalent conductivity in the control volume is accurately calculated by using a series-parallel resistance formula, and the precision defect of a traditional method in a complex electrical structure is effectively solved. According to the method, the calculation efficiency and the numerical precision are effectively balanced, and the optimal discretization modeling of the transient electromagnetic response is realized.

The invention provides a multidisciplinary coupling numerical simulation generality algorithm library and a parallel computing method, and relates to the field of aviation multidisciplinary coupling numerical simulation. Comprising a parallel coupling format library, a data mapping library, a grid support library, a data bus library, an MPI parallel library and a subject calculation interface library which are used for providing common algorithm support in multidisciplinary coupling simulation. According to the parallel computing method, a two-layer parallel architecture based on multiple plug-ins, multiple data and MPI is adopted, parallel in subjects, parallel between subjects, distributed data exchange and cross-process communication are achieved, and the parallel efficiency and numerical precision of coupling simulation are improved. The method is suitable for pneumatic + X type multidisciplinary coupling simulation, supports flexible expansion of disciplinary types and number, and has good parallel expandability and coupling precision.

In order to solve the problems that in the prior art, calculation precision and efficiency are difficult to consider at the same time during supersonic jet machining, and detached shock waves cannot be accurately captured, the invention provides a finite element simulation method for the stagnation flexible constraint flow field characteristics of a jet impact area. The finite element simulation method comprises the steps that firstly, a geometric model of a jet area and an outer watershed is established, and preliminary solving is conducted; extracting jet flow axis pressure gradient characteristics to determine detached shock wave interval coordinates; then reconstructing a block geometric model comprising an upper jet flow region, a detached shock wave region, a stagnation region and an outer watershed according to the coordinates, and implementing differential local grid encryption on the shock wave region and the stagnation region; and finally, carrying out second iterative solution based on the Sutherland viscosity law to obtain final flow field distribution. According to the method, the thickness and the position of the shock wave layer are accurately captured while the number of the grids is controlled through the block encryption strategy, the contradiction between the calculation economy and the numerical precision under the transonic flow condition is solved, and the method is suitable for numerical simulation of the complex supersonic gas jet flow field containing the detached shock wave and the stagnation region.

The invention discloses a gradient numerical calculation method and system based on grid self-adaption and a hybrid strategy, and the method comprises the steps: obtaining all grid information in a to-be-calculated region, and carrying out the recognition of grid positions; judging whether the grid belongs to an internal grid or not according to the identified grid position; if the grid does not belong to the internal grid, grid center gradient calculation is carried out based on a Green-Gauss method, and a grid center gradient is obtained; if the grid unit belongs to the internal grid, calculating the grid distortion of all the internal grids based on a grid distortion calculation method of different types of grid units; calculating a weighting coefficient according to the grid torsion degree; according to different weighting coefficients, calculating grid core gradients by adopting different grid core gradient calculation methods; and calculating a gradient value of the interface based on the grid core gradient of the adjacent grid units of the interface. According to the method, the numerical precision, the adaptability, the stability and the calculation efficiency of the overall grid gradient are improved, and a new thought is provided for high-precision numerical discrete solution of computational fluid mechanics.

The application discloses a kind of double box girder flow field dynamics modal and flow stability analysis method and system, belong to bridge engineering field.This method first establishes double box girder flow field model by computational fluid dynamics, solves the momentum equation and continuity equation of two-dimensional incompressible flow;Dynamic modal decomposition is carried out again, and eigenvalue, characteristic mode and other information are obtained;Finally, based on linear stability theory, the flow field component is expressed as the sum of steady-state solution and disturbance term, and the global linear flow stability equation is obtained by substituting into the equation, and the flow stability is analyzed by discrete.This method can accurately extract the modal characteristics of each order, providing a scientific basis for wind-induced vibration analysis of double box girder, and has important application value.The method of the application occupies less computing resources, has high numerical accuracy, and can be further expanded to higher precision calculation model according to computing resources, and is suitable for application in actual bridge engineering.

The application relates to the technical field of engineering simulation, and discloses an industrial structure mechanics simulation prediction system based on an enhanced graph attention network, which comprises a finite element data mapping and enhancement module, a prediction module, an optimization module and a deployment module. The system reconstructs a finite element grid into a graph structure tensor, captures geometric mutation characteristics by using high-dimensional feature projection and adaptive feature recalibration, and transmits deep physical information through dense residual connection. In the training stage, a physical constraint loss function containing attention area weighting and structure smoothness is introduced; in the reasoning stage, a mixed precision mode is adaptively switched according to hardware attributes, and large graph blocking and fusion calculation based on overlapping boundaries are performed. The application effectively solves the contradiction between calculation efficiency and numerical accuracy in complex industrial structure mechanics response prediction, and realizes millisecond-level high-fidelity online simulation.

The application discloses a large language model low-delay inference method based on dynamic inference graph optimization, and proposes a low-delay inference method based on dynamic inference graph optimization; a rewritable and replayable template inference graph is constructed, and a template library is established according to an input shape vector; during inference, a template is matched according to a distance threshold value, and only attention / forward subgraphs are locally recaptured when the threshold value is exceeded; a forward execution graph injects a key-value cache page pointer, a numerical precision and an adapter identifier, and is replayed; pre-populated and decoded subgraphs are divided to implement a graph-level scheduling; a key operator is switched between a standard / quick kernel and different precisions during operation; a page-level rollback of a speculative branch is realized through a shadow page table and a reference count, and a batch and a template selection are adaptively adjusted based on online indicators; compared with existing schemes, the application reduces recapture and startup overhead, suppresses tail delay and jitter, and improves hardware utilization and service stability.

The application provides a steel structure calculation method and system based on physical logic anchoring and heterogeneous decoupling, and relates to the technical field of intelligent steel structure calculation. The method comprises the following steps: extracting user's unstructured engineering requirements into a structured feature vector, constructing a directed acyclic computation graph along a knowledge graph; starting knowledge graph reasoning and retrieval enhancement in parallel to generate a two-way evidence, calculating a logic anchoring coefficient Lambda, which includes a number version consistency judgment, a specification timeliness dynamic weight and a strong item penalty, comparing Lambda with a dynamic reliability threshold and executing a three-branch decision to eliminate index illusion; routing the calculation instruction to a physically isolated sandbox after triple audit to determine the solution and eliminate calculation illusion; based on the logic dependence hypergraph, the dissenting node is traced back and locally recalculated, and a compliant calculation report is output. From the architecture level, the calculation illusion and index illusion in the steel structure calculation are eliminated, and the industrial numerical precision and reliable specification compliance traceability are realized.

The present application relates to the technical field of computers. Provided are a floating-point number processing method, and a computing device and a computing chip. The computing device acquires a first floating-point number, and obtains a second floating-point number on the basis of a first sign field, a first exponent field and a first mantissa field of the first floating-point number. The data format used by the second floating-point number comprises a shared data part and a variable data part, wherein the shared data part is stored in a shared area of a memory, and the shared data part is shared by a plurality of pieces of data within the data range to which the first floating-point number belongs. In the shared area of the memory, a common shared data part may be stored for a plurality of data, thereby facilitating the representation of floating point numbers by using limited bit widths. The shared data part comprises a second exponent field and a second mantissa field, wherein the second mantissa field of the shared data part may be used for compensating for the precision of values that can be represented by the variable data part, thereby improving the numerical precision of data that can be represented by the floating-point numbers, and thus satisfying the requirements of AI training and inference.

Techniques for computing matrix operations for arbitrarily large matrices on a limited size hybrid analog-digital matrix processor are described. Techniques for gain adjustment in a limited size hybrid analog-digital matrix processor are described that enable the system to achieve higher energy efficiency, greater physical density, and improved numerical accuracy. In some embodiments, these techniques maximize the prediction accuracy of GEMM-based convolutional neural networks using low-precision data representations.

The invention relates to the technical field of a cesium feed-in system in a magnetic confinement nuclear fusion negative neutral beam system, in particular to an optimization method for DSMC numerical simulation of a cesium feed-in system based on CUDA parallel. According to the technical scheme, the method comprises the following steps: setting a molecular initial state at a host end, and preparing initial conditions for analog computation; allocating a memory space for the molecular array and the related data structure at the equipment end; molecular motion is calculated in parallel through a GPU at an equipment end, and collision between molecules and a wall surface is processed. According to the method, the simulation efficiency is improved to the minute level through a CUDA parallel computing architecture, a grid-based molecular index and memory access mechanism is optimized, the statistical reliability is guaranteed by adopting a thread-independent random number generator, near-vacuum molecular index is accurately restored by means of a decoupled physical model, and the statistical reliability is improved. Therefore, an efficient and practical analysis tool is provided for cesium feed-in system engineering design on the premise that the numerical precision is guaranteed.

The invention relates to a rapid target detection method based on an RK3588 platform, and solves the problem that the real-time requirement is difficult to meet because the frame rate is often lower than 10fps when a target detection algorithm is directly operated in equipment. According to the method, the operation efficiency of a target detection algorithm on an RK3588 platform can be greatly improved through the methods of modifying a model structure and an activation function, quantifying the model, using a thread pool and the like on the basis of a yov8s target detection model, and the actual measurement frame rate can exceed 100fps. And finally, high-frame-rate target detection on the edge computing equipment is realized. The method specifically comprises; the method comprises the following steps: 1) modifying a model structure, replacing an activation function, removing a decoding operation on a bounding box in a YOLOv8 model, and executing the decoding operation on the bounding box in a CPU (Central Processing Unit); 2) quantizing the model trained by the RK3588 platform so as to better exert the performance of the NPU, specifically including (1) numerical precision reduction, (2) linear transformation, (3) thread pool acceleration: accelerating model reasoning by using a thread pool, and (4) target detection.

The invention discloses an unconditionally stable UPML electromagnetic simulation method based on Dosin FDTD, and belongs to the field of computational electromagnetics and numerical analysis, and the method comprises the steps: introducing an anisotropic damping term under a Dosin finite difference time domain (MS-FDTD) framework, embedding the UPML absorption characteristic into a Dosin Maxwell system, and carrying out the UPML electromagnetic simulation. And the consistency of the main domain and the absorption layer in geometric structure and energy transfer is realized. By adopting a Preissmann implicit discrete format and a local one-dimensional operator splitting strategy, a numerical value updating form irrelevant to a time step and a space step is obtained, so that unconditional stable solution is realized. According to the method, the electromagnetic field quantity is decoupled through the intermediate variable of the electric displacement vector and the magnetic induction intensity, and the solving efficiency and the numerical precision are improved. The provided Dosin UPML method can keep energy conservation and structural stability in complex media and long-time simulation, and the electromagnetic wave absorption precision and simulation reliability are remarkably improved.

The embodiment of the invention provides an operator quantification method and device based on mixing precision, equipment and a storage medium, and belongs to the field of model quantification. The method comprises the following steps: acquiring an operator feature data set of a neural network model to be quantized, wherein the operator feature data set comprises at least two quantitative indexes of the sensitivity of each operator in the neural network model to quantization; based on a preset clustering algorithm, performing clustering processing on the operator feature data set to obtain at least two clustering clusters, and classifying operators corresponding to feature data in each clustering cluster into one group to obtain at least two operator groups; according to at least two quantitative indexes of the clustering center of each clustering cluster, distributing corresponding numerical value precision to each operator in each operator group; and according to the numerical value precision corresponding to each operator, performing quantification processing on the neural network model. According to the method, appropriate numerical value precision can be accurately distributed to different operators in the neural network model, so that the precision of the quantized neural network model is ensured.

The invention provides a tensor calculation coprocessor which comprises a preprocessing remapping module, a scheduling calculation module and an output mapping module. The preprocessing remapping module is used for receiving task parameters of a tensor calculation task transmitted based on a preset bus and matrix data of each to-be-processed matrix and mapping the task parameters and the matrix data to the input cache unit; the scheduling calculation module is used for scheduling the task parameters from the input cache unit to the target calculation array and scheduling the matrix data to the target calculation array in a blocking manner for tensor calculation to obtain a calculation result and a block index; and the output mapping module is used for mapping the calculation result to the output cache unit according to the block index. The coprocessor supports hardware data preprocessing remapping, an AXI universal interface and flexible parameter configuration of the coprocessor and supports various numerical value precisions and matrix calculation sizes, the overall calculation efficiency of the tensor calculation coprocessor is improved, and the comprehensive advantages of high flexibility and high adaptability are achieved.

The invention discloses a device for calculating logarithm of complex amplitude, a signal processing method, a device, an integrated circuit, an electromagnetic wave device and electronic equipment, which are applied to the field of digital signal processing and are used for calculating and outputting by taking the sum of absolute values of a real part and an imaginary part of a complex as input based on a lookup table. Taking the data as an input, and calculating and outputting the data based on the lookup table; ,...; the embodiment of the invention does not need a multiplier, and the hardware area is small. Compared with the prior art, the method has the advantage of higher cost, and can also achieve higher numerical precision when the lookup table is used for calculating the logarithm.

The application discloses a quantum-inspired aero-engine combustion multi-scale operator adaptive weighting and numerical solving method and system and a storage medium, and belongs to the technical field of computational fluid dynamics and combustion numerical simulation. The method comprises the following steps: in the combustion numerical simulation process, control equations of an aero-engine combustion process are decomposed into multiple physical operators; in the numerical iteration process, contribution characteristic quantities of each physical operator are calculated online; a quantum-inspired weight state vector is constructed based on the contribution characteristic quantities; the weight state is adaptively updated according to the contribution characteristic quantity change; the solving strategy of each physical operator is adaptively scheduled based on the updated weight state; and time advancing is completed and results are output. The application can dynamically respond to the change of a dominant physical mechanism in the combustion process, and significantly improves the calculation efficiency and numerical stability under the premise of ensuring the numerical precision and conservation.

This invention belongs to the field of meteorological data compression technology, and discloses a meteorological data compression method, system, device, and medium based on a variational autoencoder, to solve the problems of low compression ratio, poor reconstruction accuracy at high compression ratios, high computational complexity, and lack of targeted processing for uneven latitude and longitude distribution in existing technologies. The method of this invention includes: acquiring meteorological data to be compressed; performing latitude and longitude location encoding; inputting the encoded data into the encoder of a trained variational autoencoder model, which extracts multi-scale features and outputs low-dimensional latent representations through alternating stacked residual convolutional units and sliding window self-attention units; quantizing the representations to generate a compressed bitstream; and inputting the compressed bitstream into a decoder to reconstruct the meteorological data. This invention achieves extremely high compression ratios while significantly maintaining the numerical accuracy of meteorological data and the structural characteristics of extreme events, and also considers computational efficiency.

The invention discloses a wind field downscaling prediction method and system based on 3D space-time decoupling. The method comprises the steps that a three-dimensional time sequence meteorological field output by a coarse resolution numerical weather forecast mode is acquired to serve as input data, and corresponding high-resolution large-eddy simulation output serves as label data; a three-dimensional downscaling hybrid deep learning model is constructed, the model adopts a three-dimensional Fourier neural operator to extract three-dimensional space global features, sequential evolution modeling is carried out through a parallel convolution long and short term memory network after vertical dimension decoupling slicing, re-stacking is carried out, and a high-resolution three-dimensional prediction wind field is output through sub-pixel convolution up-sampling; a composite loss function containing data driving loss and physical information loss is adopted for training, and the physical information loss comprises three-dimensional mass conservation loss, gravity wave loss and advection loss. According to the method, the end-to-end rapid reconstruction of the three-dimensional wind field from the coarse resolution to the high resolution is realized, and the physical consistency of the prediction result is ensured while the numerical precision is ensured.

The application discloses a quantum-inspired aero-engine combustion multi-scale operator adaptive weighting and numerical solving method and system and a storage medium, and belongs to the technical field of computational fluid dynamics and combustion numerical simulation. The method comprises the following steps: in the combustion numerical simulation process, control equations of an aero-engine combustion process are decomposed into multiple physical operators; in the numerical iteration process, contribution characteristic quantities of each physical operator are calculated online; a quantum-inspired weight state vector is constructed based on the contribution characteristic quantities; the weight state is adaptively updated according to the contribution characteristic quantity change; the solving strategy of each physical operator is adaptively scheduled based on the updated weight state; and time advancing is completed and results are output. The application can dynamically respond to the change of a dominant physical mechanism in the combustion process, significantly improves the calculation efficiency and numerical stability under the premise of ensuring the numerical precision and conservation.

The invention discloses a model training method and device based on distributed computing, equipment and a program product. According to the scheme, Monte Carlo simulation is executed on a mixed precision layer in a neural network to evaluate the comprehensive performance of multiple candidate mixed precision configuration strategies, the mixed precision layer adopts at least two kinds of numerical precision to execute calculation in training, and the comprehensive performance of the mixed precision layer is evaluated through indexes such as the precision standard-reaching rate, resource consumption and training efficiency. And selecting a target mixing precision configuration strategy from the plurality of candidate mixing precision configuration strategies according to the comprehensive performance of each candidate mixing precision configuration strategy on the mixing precision neural network layer, inputting the target mixing precision configuration strategy into the hypergraph convolutional neural network for learning rate optimization, and determining a target learning rate matched with the target mixing precision configuration strategy. And finally, distributing the target strategy and the target learning rate to each distributed computing node so as to guide the configuration of the distributed computing nodes and cooperatively complete the training task of the neural network. The efficiency and accuracy of large-scale model training are effectively improved.

The invention provides a video content intelligent analysis method based on an edge computing platform, and the method comprises the steps: collecting a video stream through an image collection interface of the edge computing platform, and carrying out the preprocessing of a video frame at a CPU end, and obtaining standardized image data; the method comprises the following steps: converting a pre-trained neural network model into a special format model adaptive to an NPU (Network Processing Unit), and adopting a hierarchical quantization strategy in the conversion process: quantizing at least one network layer responsible for basic feature extraction in the model by adopting first numerical value precision, quantizing other network layers responsible for classification and regression in the model by adopting second numerical value precision lower than the first numerical value precision; dynamically scheduling the number of processing cores in an activated state in the NPU based on a processing load determined by analyzing the current video frame content represented by the standardized image data, and utilizing the processing cores in the activated state to load a special format model to infer the standardized image data; and post-processing the reasoning output result of the NPU to generate a structured analysis result.

Apparatus and methods for training a neural network accelerator using quantized precision data formats are disclosed, and in particular for storing activation values from a neural network in a compressed format for use during forward and backward propagation training of the neural network. In certain examples of the disclosed technology, a computing system includes processors, memory, and a compressor in communication with the memory. The computing system is configured to perform forward propagation for a layer of a neural network to produced first activation values in a first block floating-point format. In some examples, activation values generated by forward propagation are converted by the compressor to a second block floating-point format having a narrower numerical precision than the first block floating-point format. The compressed activation values are stored in the memory, where they can be retrieved for use during back propagation.

Disclosed in the embodiments of the present disclosure are a simulation method and apparatus for a semiconductor device containing a quantum confinement region, and a device. The method comprises: on the basis of information of a target location in which at least one quantum confinement region is located, partitioning into at least two structural sub-models a structural model corresponding to a semiconductor device to be simulated; for an interface region between the structural sub-models, the spatial locations of which are adjacent, generating at least one low-dimensional mesh element, so as to form a low-dimensional mesh, and on the basis of the low-dimensional mesh, generating a high-dimensional mesh element for the adjacent structural sub-models, so as to form a high-dimensional mesh, such that a target structural model corresponding to said semiconductor device is obtained when the meshes are constructed; and determining, on the basis of a preset electrical energy equation set, the electrical energy distribution at each mesh point in the target structural model corresponding to said semiconductor device. Performance loss caused by interpolation between two meshes in different dimensions during the numerical simulation solving of a semiconductor device is avoided, thereby improving the computational efficiency and numerical accuracy of the simulation of the semiconductor device.

The application provides a virtual speed limit based maglev train operation speed curve simulation optimization method, proposes a time-space domain hybrid adaptive discretization method, can dynamically switch time domain and space domain calculation modes according to train speed, guarantees time resolution in the low speed section, keeps space resolution in the high speed section, fundamentally eliminates the speed dependent error caused by the single domain method, and realizes the consistency of numerical accuracy in the whole speed domain. In addition, the application introduces virtual speed limit as the core decision variable, converts the complex high-dimensional continuous control problem into a structured discrete input sequence problem, realizes the indirect control of the traction, cruise, coasting and braking process by adjusting the virtual speed limit distribution, and significantly reduces the optimization dimension. With the virtual speed limit block division mechanism and the embedded constraint processing strategy, the method can automatically generate a physically feasible optimal speed curve under the conditions of double speed protection, safe braking distance and passenger comfort and the like.

Embodiments of the present disclosure relate to training methods for training mixed-precision neural networks, particularly neural material networks. In an embodiment, a mixed-precision neural material network includes input, hidden, and output layers having different numerical precisions that is trained or fine-tuned to encode and / or decode compact latent code representations.

The invention discloses a floating-point number processing method, computing equipment and a computing chip, and relates to the technical field of computers. The computing device obtains a first floating-point number, and obtains a second floating-point number based on a first symbol field, a first order code field and a first mantissa field of the first floating-point number. The data format adopted by the second floating-point number comprises a shared data part and a variable data part, the shared data part is stored in a shared area of the memory, and the shared data part is shared by multiple pieces of data in a data range to which the first floating-point number belongs. A common shared data portion may be stored in a shared region of the memory for multiple data, facilitating the use of limited bit widths to express floating-point numbers. And the shared data part comprises a second-order code domain and a second mantissa domain, and the second mantissa domain of the shared data part can be used for supplementing the numerical precision which can be represented by the variable data part, so that the numerical precision of data which can be represented by the floating-point number can be improved, and the requirements of AI training and reasoning are met.

The optimization method, device, equipment and medium provided by the embodiments of the present application are used to improve resource utilization and execution efficiency. In the embodiments of the present application, the implementation mode of traditional step-by-step disassembly and round-by-round execution is broken through structural optimization at the algorithm level, and a two-round calculation method is proposed. Without sacrificing the functional integrity and numerical accuracy, the calculation complexity and resource consumption of the quantized Addrmsnorm algorithm are significantly reduced. Compared with the implementation mode of three times of W-axis cycle in the prior art, the method provided by the embodiments of the present application has higher execution efficiency, and is more friendly to general-purpose processors such as CPUs.