6results about How to "The acceleration effect is obvious" patented technology

This application discloses a data processing method, apparatus, neural network accelerator, and storage medium. The apparatus includes: a conversion processing module, configured to perform a first matrix transformation operation in the Winograd algorithm on the corresponding image data for each channel in a feature image, and perform a second matrix transformation operation in the Winograd algorithm on the corresponding convolution kernel, to obtain the corresponding converted image data and convolution kernel; at least one convolution processing unit, including a number of calculation groups matching the number of channels in the feature image, each calculation group supporting the execution of a dot product operation in the Winograd algorithm on the converted image data and convolution kernel corresponding to one channel in the feature image, to obtain the corresponding dot product data; the conversion processing module is further configured to accumulate the dot product data corresponding to different channels in the feature image, and perform a third matrix transformation operation in the Winograd algorithm on the obtained accumulated data, to obtain the convolution processing result corresponding to the feature image.

The invention relates to the technical field of neural network acceleration, and relates to a CPU-FPGA-oriented graph neural network training acceleration method and system. The method comprises the steps that a CPU stores complete graph feature data and distributes different feature data to storage modules of different FPGAs through data channels according to a load balancing algorithm; partitioning the GNN network model by the CPU, and matching a partitioned model operator with a corresponding FPGA (Field Programmable Gate Array); the CPU completes issuing and sampling of a partition model according to a sampler algorithm, and sends the partition model to the FPGA; reading feature data after the FPGA is started, completing a calculation task by a load execution module, and writing a result back to a CPU memory; the CPU completes data updating and weight updating; and resending the updated weight data to the corresponding FPGA, and carrying out iterative training. According to the invention, operation of the graph neural network on a CPU-FPGA platform is successfully realized, and the overall acceleration effect is excellent.

This invention discloses an intelligent adaptive dose verification calculation method and system. The intelligent adaptive dose verification calculation method includes: obtaining preliminary dose distribution data for a patient based on a deep learning-based dose prediction model; dynamically assigning variance reduction parameters to particles in a Monte Carlo simulation based on the high and low dose distribution information in the preliminary dose distribution data, wherein the variance reduction parameters include simulation weights, with simulation weights assigned to particles predicted to be in high-dose regions being less than those assigned to particles predicted to be in low-dose regions; and using a Monte Carlo dose calculation engine, simulating the planned beam based on the dynamically assigned variance reduction parameters to obtain high-precision verification dose distribution data. This invention's intelligent adaptive dose verification calculation method ensures the physical authenticity of the final verification dose distribution and the credibility of the "gold standard."

The present application relates to the technical field of quantum search simulation, and particularly relates to a quantum search simulation method and system for supercomputing, which constructs a quantum search simulation heterogeneous execution environment, the heterogeneous execution environment is composed of a general processor CPU host end and a coprocessor DCU device end, and the general processor CPU interacts with the coprocessor DCU through a communication bus; in the heterogeneous execution environment, the current computing task scale is determined according to the input number of quantum bits and the target item to be searched, the quantum register is created on the general processor CPU according to the search algorithm circuit diagram, the memory space for storing the probability amplitude data is created on the coprocessor DCU, and the equal-weight superposition state is initialized and prepared, the G iteration process of the quantum search algorithm is simulated, and the target item probability amplitude is obtained through G iteration. The present application transplants the quantum gate operation with intensive calculation to the DCU accelerator for execution, realizes the heterogeneous version running of the quantum search algorithm on the supercomputing platform, has strong scalability, and improves the computing performance of the quantum search simulation platform.

The application discloses a reinforced concrete structure improvement time domain synthetic aperture imaging fast algorithm, and steps are as follows: a reinforced concrete two-dimensional finite element simulation model is established, and a half diffusion angle is calculated; simulation testing is carried out on an ultrasonic probe in the reinforced concrete two-dimensional finite element simulation model, and full matrix data are formed; the reinforced concrete two-dimensional finite element simulation model is pixelated, and a diffusion angle factor is calculated; whether synthetic aperture processing is carried out is compared according to the half diffusion angle in step one and the diffusion angle factor in step three; and imaging processing is carried out on the full matrix data in step two according to a time domain synthetic aperture full focusing algorithm. The application has the beneficial effects that the application determines the diffusion angle factor of each pixel point to each transmission ultrasonic probe, the area outside the diffusion angle of the ultrasonic probe is not subjected to sound path calculation and amplitude superposition, the algorithm efficiency is greatly improved, meanwhile, imaging of the pseudo defects outside the diffusion angle is avoided. The smaller the diffusion angle is, the more the data quantity is, and the more obvious the algorithm acceleration effect is.

The invention relates to an irradiation plan optimization method based on novel sparse sampling, belongs to the technical field of radiation irradiation dose optimization, and solves the problems that invalid voxel data volume cannot be effectively simplified, the processing data volume is large and the result is not accurate enough in the prior art. Receiving an initial irradiation plan and generating a full dose deposition matrix corresponding to each radiation field; constructing a gradient image based on the full dose deposition matrix; performing dual-condition voxel screening on the constructed gradient image to obtain a final sampling voxel set, and constructing a sparse dose deposition matrix; and performing irradiation plan optimization and dose normalization on the sparse dose deposition matrix to obtain an optimized irradiation plan.