Abstract: Provided are a signal processing method based on a many-core chip, an electronic device and a medium. The method includes: determining, according to a time domain signal to be processed and a time-frequency transform type of the time domain signal, a transform kernel matrix of the time domain signal; mapping the transform kernel matrix to a plurality of processing cores of the many-core chip; and mapping the time domain signal to the plurality of processing cores so that the plurality of processing cores determine, according to the transform kernel matrix and the time domain signal, a frequency domain signal corresponding to the time domain signal.

Abstract: The present disclosure relates to a synaptic weight training method, a target identification method, an electronic device and a storage medium. The synaptic weight training method includes: inputting spike signals corresponding to training samples into a spiking neural network to be trained; learning a target synaptic weight in the spiking neural network with a back propagation rule to obtain a first branch weight; and learning the target synaptic weight with a synaptic plasticity rule to obtain a second branch weight; and updating the target synaptic weight according to the first branch weight and the second branch weight.

Abstract: Provided is a method for training spiking neuron network, including: inputting a spiking signal corresponding to a training sample into a spiking neuron network to be trained; and training the spiking neuron network based on a back propagation rule, wherein a forward propagation stage of training includes: adjusting a spiking firing threshold corresponding to a target layer in the spiking neuron network according to confidence corresponding to the target layer. Also provided are a method for processing data based on a spiking neuron network, an electronic device, and a non-transitory computer-readable medium.

Abstract: A computing core circuit, including: an encoding module, a route sending module, and a control module, wherein the control module is configured to control the encoding module to perform encoding processing on a pulse sequence determined by pulses of at least one neuron in a current computing core to be transmitted, so as to obtain an encoded pulse sequence, and control the route sending module to determine a corresponding route packet according to the encoded pulse sequence, so as to send the route packet. The present disclosure further provides a data processing method, a chip, a board, an electronic device, and a computer-readable storage medium.

Abstract: The present disclosure relates to a data storage method, a data acquisition method, and a data acquisition apparatus for a weight matrix, and a device. The data storage method includes: generating a coding array and a special value table according to the weight matrix; and storing the coding array and the special value table. The coding array and the special value table occupy a storage space smaller than the weight matrix.

Abstract: The present disclosure relates to an image processing method and device, a storage medium and an electronic device. The image processing method includes: acquiring a current frame image, and performing semantic feature extraction processing on the current frame image to obtain a semantic feature set of the current frame image; determining a historical frame image matched with the current frame image, and acquiring frame number information of the historical frame image; and generating a compressed information packet according to the semantic feature set of the current frame image and the frame number information of the historical frame image, and storing and/or transmitting the compressed information packet. Thus, the image processing method can increase an image compression ratio while ensuring image quality, thereby allowing image information to be transmitted and stored conveniently.

Abstract: The present disclosure relates to a synaptic weight training method, a target identification method, an electronic device and a storage medium. The synaptic weight training method includes: inputting spike signals corresponding to training samples into a spiking neural network to be trained; learning a target synaptic weight in the spiking neural network with a back propagation rule to obtain a first branch weight; and learning the target synaptic weight with a synaptic plasticity rule to obtain a second branch weight; and updating the target synaptic weight according to the first branch weight and the second branch weight.

Abstract: The present disclosure provides a method for converting numerical values into spikes. The method includes: generating an initial spike sequence according to input numerical values, where the initial spike sequence includes at least one data string, each of which is independently selected from one of a consecutive spike train or a consecutive non-spike train, the number of spikes in all of the at least one data string is equal to an expected value of the number of spikes in a target spike sequence to be generated, and the target spike sequence to be generated is a spike sequence of a spiking neural network within a time period; and randomly selecting and modifying data from the initial spike sequence, so as to form the target spike sequence. The present disclosure further provides an apparatus for converting numerical values into spikes, an electronic device, and a computer-readable storage medium.

Abstract: A video retrieval method and apparatus, a device and a storage medium are provided. The method comprises the following steps: acquiring a comparison video clip from a video library according to the duration of a to-be-tested video (S110); determining the similarity between the to-be-tested video and the comparison video clip by a target spatio-temporal neural network, a spatio-temporal convolutional layer of the target spatio-temporal neural network being configured to be capable of performing two-dimensional convolution and temporal dimension information processing, respectively (S120); and traversing the video library, and outputting a retrieval result according to the similarity (S130).

Abstract: A video retrieval method and apparatus, a device and a storage medium are provided. The method comprises the following steps: acquiring a comparison video clip from a video library according to the duration of a to-be-tested video (S110); determining the similarity between the to-be-tested video and the comparison video clip by a target spatio-temporal neural network, a spatio-temporal convolutional layer of the target spatio-temporal neural network being configured to be capable of performing two-dimensional convolution and temporal dimension information processing, respectively (S120); and traversing the video library, and outputting a retrieval result according to the similarity (S130).

Abstract: Provided is a method for training spiking neuron network, including: inputting a spiking signal corresponding to a training sample into a spiking neuron network to be trained; and training the spiking neuron network based on a back propagation rule, wherein a forward propagation stage of training includes: adjusting a spiking firing threshold corresponding to a target layer in the spiking neuron network according to confidence corresponding to the target layer.

Abstract: The present disclosure provides a method for converting numerical values into spikes. The method includes: generating an initial spike sequence according to input numerical values, where the initial spike sequence includes at least one data string, each of which is independently selected from one of a consecutive spike train or a consecutive non-spike train, the number of spikes in all of the at least one data string is equal to an expected value of the number of spikes in a target spike sequence to be generated, and the target spike sequence to be generated is a spike sequence of a spiking neural network within a time period; and randomly selecting and modifying data from the initial spike sequence, so as to form the target spike sequence. The present disclosure further provides an apparatus for converting numerical values into spikes, an electronic device, and a computer-readable storage medium.

Abstract: A computing core circuit, including: an encoding module, a route sending module, and a control module, wherein the control module is configured to control the encoding module to perform encoding processing on a pulse sequence determined by pulses to be transmitted of at least one neuron in a current computing core, so as to obtain an encoded pulse sequence, and control the route sending module to determine a corresponding route packet according to the encoded pulse sequence, so as to send the route packet. The present disclosure further provides a data processing method, a chip, a board, an electronic device, and a computer-readable storage medium.

Abstract: A behavior recognition method and system, including: dividing video data into a plurality of video clips, performing frame extraction processing on each video clip to obtain frame images, and performing optical flow extraction on the frame images to obtain optical flow images; performing feature extraction on the frame images and the optical flow images to obtain feature maps of the frame images and the optical flow images; performing spatio-temporal convolution processing on the feature maps of the frame images and the optical flow images, and determining a spatial prediction result and a temporal prediction result; fusing the spatial prediction results of all the video clips to obtain a spatial fusion result, and fusing the temporal prediction results of all the video clips to obtain a temporal fusion result; and performing two-stream fusion on the spatial fusion result and the temporal fusion result to obtain a behavior recognition result.

Abstract: The disclosure relates to a self-adaptive leakage value neuron information processing method and system. The method includes: receiving front end pulse neuron output information; reading current pulse neuron information, wherein the current pulse neuron information includes self-adaptive membrane potential leakage information; calculating current pulse neuron output information according to the front end pulse neuron output information and the current pulse neuron information; updating the self-adaptive membrane potential leakage information according to the current pulse neuron output information; outputting the current pulse neuron output information.

Abstract: There are provided a neural network weight discretizing method, system and device, and a computer readable storage medium. The method includes acquiring a weight value range and a number of discrete weight states, the weight value range referring to a range of discrete weight values consisting of a maximum weight value of a current time step and a minimum weight value of the current time step, and the number of discrete weight states referring to the quantity of discrete weight states. The method also includes acquiring a weight state of a previous time step and a weight increment of the current time step and acquiring a state transfer direction by using a directional function according to the weight increment of the current time step. The method also includes acquiring a weight state of the current time step according to the weight state of the previous time step, the weight increment of the current time step, the state transfer direction, the weight value range and the number of discrete weight states.

Abstract: The present disclosure provides a brain-like computing chip and a computing device. The brain-like computing chip includes is a many-core system composed of one or more functional cores, and data transmission is performed between the functional cores by means of a network-on-chip. The functional core includes at least one neuron processor configured to compute various neuron models, and at least one coprocessor coupled to the neuron processor and configured to perform an integral operation and/or a multiply-add-type operation; and the neuron processor is capable of calling the coprocessor to perform the multiply-add-type operation.

Abstract: A virtual image processing method, an image processing system, and a storage medium used for realizing virtualization of a captured picture, the method includes the following steps: acquiring a reference position and a real-time position of a camera device; calculating a movement parameter of the real-time position relative to the reference position; virtualizing, according to the movement parameter, the position and the posture of the virtual camera device, and calculating a perspective parameter of a scene to be replaced with respect to the virtualized virtual camera device; encoding a virtual scene according to the perspective parameter; and using the encoded virtual scene to replace the scene to be replaced.

Abstract: An LED display screen, includes: a frame, modules disposed at a side of the frame and including an LED light board, and a non-display surface of the LED light board is provided with a first socket configured to be connected with a board-to-board connector; and a power box disposed inside the frame and located between the modules, with a control board connected with the LED light board via the board-to-board connector; wherein the control board comprises a main control board and at least one sub-control board, a surface of the sub-control board facing a surface of the LED light board is provided with a second socket configured to match with the first socket, each of the sub-control boards is fixed by an elastic mechanism and a bottom surface of the power box opposite to the LED light board.

Abstract: The present disclosure provides a brain-like computing chip and a computing device. The brain-like computing chip includes is a many-core system composed of one or more functional cores, and data transmission is performed between the functional cores by means of a network-on-chip. The functional core includes at least one neuron processor configured to compute various neuron models, and at least one coprocessor coupled to the neuron processor and configured to perform an integral operation and/or a multiply-add-type operation; and the neuron processor is capable of calling the coprocessor to perform the multiply-add-type operation.