12 results about "Sorting network" patented technology

The invention discloses a data processing device, a data processing method, a chip and electronic equipment, and relates to the technical field of data processing. The data processing apparatus includes: a storage module configured to store data; the calculation module is coupled to the storage module and is configured to access an original data vector stored in the storage module, and the original data vector comprises m data elements; splitting the original data vector based on the target k value to obtain a plurality of first data vectors; performing odd-even merging sorting on the data elements in the first data vectors to obtain a plurality of first ordered data vectors; and merging and sorting the data elements in the plurality of first ordered data vectors to obtain top-k data elements in the m data elements. According to the data processing device, the data processing method, the chip and the electronic equipment provided by the invention, the top-k data elements can be obtained without a special hardware sorting network.

This invention discloses a multi-agent collaborative decision-making method for ramp merging based on spatiotemporal interaction capture and action priority, belonging to the field of intelligent transportation technology. The method includes: acquiring a sequence of observation information from multiple agents and obtaining a spatiotemporal interaction code through temporal encoding and self-attention interaction encoding; gating the spatiotemporal interaction code based on ramp merging features (TTMP); inputting the enhanced code into a policy priority ranking network, sampling the action generation order using Plackett-Luce, and then reordering the enhanced code, before inputting it into a decoding policy network with cross-attention to form a spatiotemporal interactive Plackett-Luce multi-agent reinforcement learning model. The model then autoregressively generates actions for each agent according to the reordered order and integrates them to form a joint action, generating vehicle control commands to control vehicles to perform collaborative driving in the ramp merging area. This invention can dynamically model the multi-vehicle interaction relationship and the action priority dependency between individual decisions in a strong interaction scenario of ramp merging, thereby improving the stability of collaborative decision-making and traffic efficiency.

This application discloses a data processing apparatus, a data processing method, a chip, and an electronic device, relating to the field of data processing technology. The data processing apparatus includes: a storage module configured to store x sets of original data vectors, each original data vector containing m data elements, where x and m are positive integers; and a computation module including a single instruction multiple data (SIMD) execution unit and a vector register group, the SIMD execution unit being coupled to the vector register group, and the vector register group being coupled to the storage module. The computation module is configured to: determine whether x is an integer multiple of the SIMD processing capability, split x, and obtain the top-k data elements in the original data vectors based on different data strategies. The algorithm of this application is flexible, requires no dedicated hardware sorting network, avoids dependence on dedicated hardware, and improves programming flexibility and versatility, facilitating the expansion and migration of large models across different hardware platforms.

A quantum source coding method includes: initializing a quantum register having a plurality of nodes: loading a unary-coded message into the quantum register; loading an address state into an address register of each message node of the quantum register; sorting the message nodes based on a data register such that the message nodes form a message-sorted sequence; and applying, for each message node at an end of the message-sorted sequence, a CNOT gate to the address state of each of the register's message nodes and output nodes. The method also includes: unsorting the message nodes; sorting the nodes based on the address register such that the nodes form a fully-sorted sequence; applying, for each pair of adjacent nodes in the fully-sorted sequence having the same address state, a CNOT gate to the data registers of each pair; and unsorting the nodes to return the fully-sorted sequence to the initial sequence.

Embodiments rank entity importance in monitored networks by analyzing network traffic to collect metrics representing relationships between entities. Network monitoring engines construct network graphs where nodes represent entities and edges represent relationships based on collected metrics. The system employs graph clustering algorithms to partition entities into logical clusters based on interaction patterns. Within each cluster, entities may be ranked by topological importance using metrics to identify primary entities that represent their clusters. Monitoring resources may be allocated preferentially to primary entities from each cluster. The topology-based ranking approach distributes monitoring resources throughout the network.

The invention provides a Top-K acceleration device and method based on RISC-V vector extension. The method comprises the following steps: generating configuration information in a configuration information lookup table through a software algorithm; the method comprises the following steps: in a hardware process, receiving a VTOPK instruction and decoding the VTOPK instruction so as to parse an operation parameter; according to the analyzed operation parameters, corresponding gating configuration information is loaded from the configuration information lookup table; according to the loaded gating configuration information, dynamically activating a comparison and exchange unit in the full-function double-modulation sequencing network through a gating clock technology; reading input vector data from the vector register file; performing anti-disruption processing and sequence processing on the input vector data; inputting the processed input vector data into the activated sorting network to carry out Top-K selection operation; and writing the result of the Top-K selection operation back to the vector register file so as to solve the problem that the Top-K selection algorithm cannot consider performance, flexibility and universality at the same time.

The invention discloses a real estate data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting a real estate right certificate image into a real estate right certificate recognition model, carrying out the semantic segmentation of the real estate right certificate image in a semantic segmentation network, and obtaining a plurality of image regions, geometric information and semantic tags; extracting text blocks in the image area in the text block sorting network, sorting the text blocks according to geometric information to obtain a text block sequence, extracting text contents from the text blocks in the text block sequence in the text recognition network, filling the text contents and semantic tags into a structured data template to obtain structured real estate data, and storing the structured real estate data in the text recognition network. According to the method, the structured real estate data is encrypted and then stored in the database, so that the structured real estate data is obtained by extracting the text content from the real estate certificate through the real estate certificate identification model and filling the text content into the structured data template, manual input is not needed, and the input efficiency and accuracy of the real estate data are improved.

The application discloses a data sorting device and method based on a fixed structure sorting network, wherein the method comprises a cache unit, a multiple selection logic unit, a data exchange network and multiple sorting networks; the multiple selection logic unit is used for receiving multiple selection control information, selecting a data matrix output by the sorting network or a data matrix stored in the cache unit based on the multiple selection control information, and transmitting the selected data matrix to the data exchange network; the data exchange network is used for performing data exchange and data rearrangement on the data matrix based on exchange network control information; and the sorting network is used for calling a cut sorting function to alternately perform row sorting or column sorting on the data matrix to obtain a sorted data vector. The application can realize efficient sorting processing on different sizes of input data, and improves the universality, scalability and hardware resource utilization efficiency of the sorting network.

The application discloses an LTE terminal-based RRC link communication intelligent monitoring method and system, relates to the technical field of communication, and comprises the following steps: acquiring network communication parameter information, acquiring network load data according to the network communication parameter information, sorting network load data of the same kind according to time sequences based on time stamps corresponding to the data according to the network load data, and acquiring data time sequence information corresponding to each kind of network load data. The application accurately analyzes the fluctuation condition of network load data through a time sliding window, is convenient for subsequent monitoring of the network load condition, selects a suitable network load monitoring frequency through a network load feature set, realizes accurate monitoring of the network load, provides a data basis for setting of RRC timing time, acquires an RRC link monitoring frequency through RRC timing time information, and ensures the stability and reliability of the RRC link.

The application discloses a data sorting device, method, chip, electronic equipment and storage medium. The device comprises a controller configured to acquire a plurality of first data groups; a parity sorting network configured to sort the first data groups into second data groups; a data selector configured to acquire a target data group; the controller is configured to compare the target data group with an i-th second data group at intervals of N bits to obtain an i-1-th third data group; when TopN sorting is performed, a double-adjusted merging network is configured to sort the i-1-th third data group into an i-1-th fourth data group; when i is equal to the number of the plurality of first data groups, the i-1-th fourth data group is taken as a TopN sorting result; when full sorting is performed, the double-adjusted merging network is configured to iteratively merge the sorted fourth data groups to obtain a full sorting result. The application can be compatible with TopN and double-adjusted full sorting algorithms and save computing resources.

The invention relates to an image restoration model training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a sample image, and repairing the sample image through an image repairing model to obtain a repaired image; determining restoration loss based on the real image and the restoration image corresponding to the sample image; outputting a quality parameter for the repaired image through the target sorting network, and determining sorting loss according to the quality parameter; the target sorting network is obtained by training based on a plurality of reference images, the plurality of reference images are obtained by repairing the same original image according to different repairing modes, and quality parameters corresponding to the plurality of reference images are matched with a good-bad relationship between repairing effects corresponding to the plurality of reference images; and performing parameter adjustment on the image restoration model based on the restoration loss and the sorting loss to obtain a trained image restoration model. By adopting the method, the training effect of the image restoration model can be improved.

The application discloses a data processing device, a data processing method, a chip and an electronic equipment, and relates to the technical field of data processing. The data processing device comprises a storage module configured to store data; and a calculation module coupled to the storage module and configured to: access an original data vector stored by the storage module, wherein the original data vector comprises m data elements; split the original data vector based on a target k value to obtain a plurality of first data vectors; perform odd-even merge sort on data elements in each of the first data vectors to obtain a plurality of first ordered data vectors; and perform merge sort on data elements in the first ordered data vectors to obtain top-k data elements in the m data elements. The data processing device, the data processing method, the chip and the electronic equipment provided by the application can obtain the top-k data elements without a special hardware sorting network.