7results about How to "Reduce computing energy consumption" patented technology

A federated learning service deployment method and system based on end node residual energy perception, equipment and medium, the method comprising: constructing a corresponding federated learning time model and an end node energy consumption model for each end node according to the federated learning process; cloud gaming according to the federated learning time model and the end node energy consumption model; edge-end matching for end nodes participating in training; the system, equipment and medium are used for the federated learning service deployment method based on the end node residual energy perception; in the end node selection stage, a cloud gaming algorithm and an edge-end matching algorithm are designed, the end nodes participating in distributed training and their computing resource allocation are determined, the residual energy of the end nodes is introduced into the gaming revenue function, the end nodes with less residual energy select a lower CPU frequency, the edge nodes accessed by the end nodes and the transmission power allocation of the end nodes are determined, and the residual energy of the end nodes is introduced into the end node preference function, thereby reducing the end node computing energy consumption and realizing end node energy protection.

The invention belongs to the technical field of computer vision, and discloses a robustness detection method for an aerial target of an unmanned aerial vehicle. A pulse neural network architecture is adopted, LIF neurons serve as basic calculation units, and invalid calculation is reduced through an event-driven sparse pulse distribution mechanism. In the feature extraction stage, a pulse adversarial interactive distillation module is introduced, L2 normalized energy constraint is applied to synaptic current, amplification and propagation of adversarial disturbance in the network are inhibited, and feature stability and detection robustness of the model under the conditions of noise interference and hostile attack are enhanced. The pulse channel characteristic refining module is based on a pulse perception-analog modulation mechanism, carries out adaptive modulation on synaptic current gain by using channel and space context information on the premise of maintaining pulse binary distribution characteristics, thereby highlighting target related characteristics and inhibiting complex background interference, and improving the target tracking precision. And the accuracy and reliability of unmanned aerial vehicle aerial image target detection are improved.

The present application relates to a kind of based on multi-degradation scene binocular image adaptive enhancement method of pulse neural network, belong to image processing field.The method includes: constructing the pulse neural network model for the binocular image enhancement of multi-degradation scene, the network model includes first branch and second branch, all using encoder-decoder structure, in the encoder block and decoder block of first branch and second branch, feature interaction is carried out through pulse stereoscopic intersection attention module;Prepare data set to train the network model constructed, and the network model trained is evaluated, whether the image recovery effect of network model reaches performance requirement is judged, if not meet the requirement, then retraining is carried out;Finally, using the network model trained and evaluated to the binocular image of multi-degradation scene is recovered.The present application can greatly reduce the calculation energy consumption, improve the calculation efficiency of pulse network, has good removal effect to rain line and raindrop in image, with universality.

The application relates to the technical field of computer data processing, and discloses a portfolio optimization method based on matrix decomposition technology and related equipment; the method comprises the following steps: obtaining portfolio data from an external memory or a local memory; the portfolio data comprises asset names of all assets in a portfolio and historical price data of a preset time period; based on the portfolio data, the average yield of the portfolio and a covariance matrix are calculated; the covariance matrix is subjected to eigenvalue decomposition to obtain a first matrix Q and a second matrix D; an auxiliary variable is constructed, a target function and a constraint condition are constructed based on the auxiliary variable; the target function and the constraint condition are input into an SQP algorithm for solving to obtain a solution of the auxiliary variable; and the weight distribution of each asset in the portfolio is calculated based on the solution of the auxiliary variable. The application can greatly improve the solving efficiency of the model, reduce the memory occupation, save the computer data processing time, and reduce the computer energy consumption.

The application relates to a vehicle control method, device, vehicle and computer readable storage medium. The method comprises: determining a target input sequence based on image information and vehicle state information of a target vehicle during driving; the target input sequence comprises a visual token sequence; performing planning and prediction processing on the target input sequence by using a planning decision module to obtain initial driving information of the target vehicle; performing adversarial reasoning on the initial driving information and the visual token sequence by using a safety review module to obtain risk warning information of the target vehicle; and performing arbitration processing on the initial driving information and the risk warning information by using an arbitration module to obtain a control instruction of the target vehicle, so that the target vehicle drives according to the control instruction. The application can improve the path decision safety of a vehicle in a complex traffic scene during automatic driving.

The invention discloses a face detection method based on a multi-memristor array, a product, equipment and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-detected face data, and carrying out the feature extraction of the face data through a target convolutional neural network, so as to obtain face feature data; wherein the face data is data collected after being authorized by a collector, and the target convolutional neural network is used for face detection; performing convolution processing on the face feature data by using each memristive array to obtain a convolution result of each memristive array and an accumulated start-stop mark of each memristive array; accumulating the convolution results according to the accumulated start-stop mark to obtain a convolution feature matrix; and constructing a convolutional layer feature map of the face data based on the convolutional feature matrix, and mapping the convolutional layer feature map into a classification result of face detection by using a classifier in the target convolutional neural network. Through the scheme, the face detection efficiency can be improved.

The invention discloses a computing task scheduling method and device and electronic equipment. The method comprises the following steps: receiving a task processing request initiated by a target object; performing multi-round analysis on task demand information of the target computing task and environment information within a preset time period before the cloud computing platform receives the task processing request by using a multi-target optimization algorithm to obtain an optimal scheduling strategy set, the optimal scheduling strategy set comprises a plurality of optimal scheduling strategies with the lowest calculation cost or the lowest calculation energy consumption or the highest calculation performance; feeding back the optimal scheduling strategy set to the target object; and in response to a strategy selection instruction initiated by a target object, selecting a corresponding optimal scheduling strategy from the optimal scheduling strategy set to schedule the target calculation task. The technical problems that the task response speed is low and the computing resource utilization rate is low due to the fact that the task scheduling strategy cannot be dynamically adjusted according to the real-time state of the network in related technologies are solved.