Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: The optical device includes: a first coupler having an adjustable beam splitting ratio; a sensing arm and a programmable modulation arm which are connected to the first coupler; and a second coupler having an input port connected to the sensing arm and the programmable modulation arm and an output port connected to a photodetector. The sensing arm is used for generating, by means of a slot waveguide, a first signal from a first light wave beam outputted by the first coupler. The programmable modulation arm is used for obtaining, by utilizing a grating, a second signal according to a second light wave beam outputted by the first coupler, and the grating is a nano grating generated under a pre-programmed voltage parameter of a programmable piezoelectric transducer of the programmable modulation arm. An electronic device and a programmable photonic integrated circuit are also disclosed herein.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: A three-dimensional reconstruction method, a system, and a non-transitory computer readable storage medium are disclosed herein. The method includes: performing local pose optimization by using a target image frame to obtain a local pose error; performing neural network prediction on the target image frame to obtain an initial reconstruction error; performing three-dimensional reconstruction according to the local pose error and the initial reconstruction error to obtain an initial reconstruction model; performing global pose optimization by using historical image frames to obtain a global optimization result and a global pose error; performing neural network completion on the global optimization result to obtain a final reconstruction error; and optimizing the initial reconstruction model according to the global pose error and the final reconstruction error to obtain a final reconstruction model.

Abstract: An image processing method, apparatus and device, and a readable storage medium are disclosed, including: obtaining a target image; inputting the target image into a quantized target deep neural network model for classification/detection to obtain an output result; and processing the target image according to a policy corresponding to the output result. A process of performing quantization to obtain the target deep neural network model includes: obtaining a pre-trained floating point type deep neural network model; extracting weight features of a deep neural network model; determining a quantization policy using the weight features; and quantizing the deep neural network model according to the quantization policy to obtain the target deep neural network model.

Abstract: An image recognition method and apparatus based on a systolic array, and a medium are disclosed. The method includes: converting obtained image feature information into a one-dimensional feature vector; converting an obtained weight matrix into a one-dimensional weight vector, and allocating a corresponding weight group to each node in a trained three-dimensional systolic array model; performing multiply-accumulate of the feature vector and a weight value on the one-dimensional feature vector in parallel by using the three-dimensional systolic array model, to obtain a feature value corresponding to each node, the feature values with different values reflecting article categories contained in an image; and determining an article category contained in the image according to the feature value corresponding to each node and a pre-established corresponding relationship between the feature value and the article category.

Abstract: An automatic driving method includes following steps: S101: acquiring real-time traffic environment information in a travel process of an autonomous vehicle at a current moment; S102: mapping the real-time traffic environment information based on a preset mapping relationship to obtain mapped traffic environment information; S103: adjusting a target deep reinforcement learning model based on a pre-stored existing deep reinforcement learning model and the mapped traffic environment information; and S104: judging whether automatic driving is finished, and in response to the automatic driving is not finished, returning to perform the step of acquiring the real-time traffic environment information in the travel process of the autonomous vehicle at the current moment. An automatic driving system, an automatic driving device and a computer medium storing the automatic driving method are further provided.

Abstract: The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Abstract: The present invention provides a method and apparatus for data caching. The method comprises: output matrixes are acquired one by one, a plurality of acquired output matrixes are written alternately into two queue sets of a first cache unit according to a sequence in which the output matrixes are acquired, and the output matrixes stored line by line in a first cache unit are written into a second cache unit one by one, according to the sequence in which the output matrixes are written into the second cache unit, valid data of each output matrix of the second cache unit is determined one by one according to preset parameters, and the valid data of each output matrix is written into a third cache unit, and the valid data of the output matrixes stored in the third cache unit are configured to be sequentially written into a memory according to a sequence in which the valid data are written into the third cache unit.

Abstract: An image recognition method and device, and a computer-readable storage medium. The method includes: inputting a sample image data set into an original neural network model in advance; and for each convolution layer of the original neural network model, using a feature map of the sample image data set in the current layer as a reconstruction target, firstly obtaining an update weight of a convolution kernel by using a kernel set construction method, then calculating an input channel combination having a minimum reconstruction error, cutting a redundant input channel to obtain a compression result of the current convolution layer, and finally, splicing compression results of the convolution layers to generate an image recognition model.

Abstract: An image recognition method and apparatus based on a systolic array, and a medium are disclosed. The method includes: converting obtained image feature information into a one-dimensional feature vector; converting an obtained weight matrix into a one-dimensional weight vector, and allocating a corresponding weight group to each node in a trained three-dimensional systolic array model; performing multiply-accumulate of the feature vector and a weight value on the one-dimensional feature vector in parallel by using the three-dimensional systolic array model, to obtain a feature value corresponding to each node, the feature values with different values reflecting article categories contained in an image; and determining an article category contained in the image according to the feature value corresponding to each node and a pre-established corresponding relationship between the feature value and the article category.

Abstract: A quantum data erasure method, system and device, and a readable storage medium. The method includes: acquiring an equal-probability quantum state system; measuring the equal-probability quantum state system to collapse the equal-probability quantum state system into a binary random number sequence; generating a corresponding random angle value according to the binary random number sequence; and performing a bitwise rotation operation on quantum data in a quantum device according to the random angle value to complete this quantum data erasure. In the present application, the introduction of a quantum true random number can ensure that erased data will not be recovered and reversely cracked, and is of great value in protecting data assets; moreover, randomly processed data still has the characteristics such as quantum coherence and quantum entanglement, and can be used in subsequent operations, whereby a time-consuming labor-intensive process of preparing a quantum system is not required every time.

Abstract: A system for accelerating an RNN network including: a first cache, which is used for outputting Wx1 to WxN or Wh1 to WhN in parallel in N paths in a cyclic switching manner, and the degree of parallelism is k; a second cache, which is used for outputting xt or ht-1 in the cyclic switching manner; a vector multiplication circuit, which is used for, by using N groups of multiplication arrays, respectively calculating Wx1xt to WxNxt, or respectively calculating Wh1ht-1 to WhNht-1; an addition circuit, which is used for calculating Wx1xt+Wh1ht-1+b1 to WxNxt+WhNht-1+bN; an activation circuit, which is used for performing an activation operation according to an output of the addition circuit; a state updating circuit, which is used for acquiring ct-1, calculating ct and ht, updating ct-1, and sending ht to the second cache; a bias data cache; a vector cache; and a cell state cache.

Abstract: A hybrid quantum-classical cloud platform and a task execution method. The cloud platform comprises: an SaaS layer for providing a user interface so as to acquire, by means of the user interface, a hybrid quantum-classical programming language corresponding to a task to be executed; a PaaS layer for performing algorithm compilation and task separation on the hybrid quantum-classical programming language to obtain a quantum computing task and a classical computing task corresponding to the task to be executed, and respectively allocating resources to the quantum computing task and the classical computing task; and an IaaS layer for executing the quantum computing task using a quantum virtual machine and executing the classical computing task using a classical server according to the resource allocation condition of the PaaS layer. Therefore, the communication overhead and the data delay can be reduced, the task processing efficiency is improved, and the quantum computing advantage is exerted.

Abstract: A neural network compression method, apparatus and device, and a storage medium are provided. The method includes: performing forward inference on target data by using a target parameter sharing network to obtain an output feature map of the last convolutional module; extracting a channel related feature from the output feature map; inputting the extracted channel related feature and a target constraint condition into a target meta-generative network; and predicting an optimal network architecture under the target constraint condition by using the target meta-generative network to obtain a compressed neural network model. By using the technical solution, the computation load of a performance evaluation process of a neural architecture search may be reduced, and the speed of the searching for a high-performance neural network architecture may be increased.

Abstract: A data processing method, system and device, and a readable storage medium. The method includes: marking each layer of a network model as a key layer or a non-key layer according to acquired structural information of the network model; respectively determining a quantization bit width range of the key layer and a quantization bit width range of the non-key layer according to hardware resource information that needs to be deployed; determining, in the quantization bit width range, optimal quantization bit widths of each layer of the network model; and training the network model based on the optimal quantization bit widths of each layer of the network model, so as to obtain an optimal network model, and performing data processing using the optimal network model.

Abstract: A method includes: setting, in a main training network, an auxiliary training network having the same architecture as the main training network, performing data enhancement on an original image to obtain an enhanced image; inputting the original image into the main training network, inputting the enhanced image into the auxiliary training network; determining whether an intersection-over-union value of a second prediction frame generated by the auxiliary training network and a target frame is greater than an intersection-over-union value of a first prediction frame generated by the main training network and the target frame; and in response to the intersection-over-union value of the second prediction frame and the target frame being greater than the intersection-over-union value of the first prediction frame and the target frame, replacing the intersection-over-union value of the first prediction frame and the target frame with the intersection-over-union value of the second prediction frame and the target f

Abstract: A hardware environment-based data quantization method includes: parsing a model file under a current deep learning framework to obtain intermediate computational graph data and weight data that are independent of a hardware environment; performing calculation on image data in an input data set through a process indicated by an intermediate computational graph to obtain feature map data; separately performing uniform quantization on the weight data and the feature map data of each layer according to a preset linear quantization method, and calculating a weight quantization factor and a feature map quantization factor (S103); combining the weight quantization factor and the feature map quantization factor to obtain a quantization parameter that makes hardware use shift instead of division; and finally, writing the quantization parameter and the quantized weight data to a bin file according to a hardware requirement so as to generate quantized file data (S105).

Abstract: Provided are a turbulence field update method, apparatus, and device, and a computer-readable storage medium. The method includes: obtaining sample turbulence data; performing model training by use of the sample turbulence data to obtain a reinforcement learning turbulence model; calculating initial turbulence data of a turbulence field by use of a Reynolds Averaged Navior-Stokes (RANS) equation; processing the initial turbulence data by use of the reinforcement learning turbulence model to obtain a predicted Reynolds stress; and performing calculation on the predicted Reynolds stress by use of the RANS equation to obtain updated turbulence data.