Abstract: A neural network processor and a control method are provided. The neural network processor includes a neural network processor cluster formed by multiple single-core neural network processors and a peripheral module. The peripheral module includes a main control unit and a DMA module. The DMA module is used to convey a first task descriptor to the main control unit. The main control unit is used to: analyze the first task descriptor, determine, according to an analysis result, a subtask to be distributed to each selected processor; modify the first task descriptor to acquire a second task descriptor respectively corresponding to each selected processor; and distribute each second task descriptor to each corresponding selected processor, and activate each selected processor to process the corresponding subtask. The main control unit schedules and manages all of the single-core neural network processors, thereby leveraging operational performance of the neural network processor.

Abstract: A neural network processor and a control method are provided. The neural network processor includes a neural network processor cluster formed by multiple single-core neural network processors and a peripheral module. The peripheral module includes a main control unit and a DMA module. The DMA module is used to convey a first task descriptor to the main control unit. The main control unit is used to: analyze the first task descriptor, determine, according to an analysis result, a subtask to be distributed to each selected processor; modify the first task descriptor to acquire a second task descriptor respectively corresponding to each selected processor; and distribute each second task descriptor to each corresponding selected processor, and activate each selected processor to process the corresponding subtask. The main control unit schedules and manages all of the single-core neural network processors, thereby leveraging operational performance of the neural network processor.

Abstract: An image processing method is configured to split a deconvolution kernel according to a preset splitting mode to obtain a sub-convolution kernel. And then, determining an original sub-matrix corresponding to the sub-convolution kernel, according to parameters of the sub-convolution kernel and an image feature matrix, and performing a convolution operation on the original sub-matrix corresponding to the sub-convolution kernel by using the sub-convolution kernel to obtain a deconvolution sub-matrix corresponding to each sub-convolution kernel; determining a target feature matrix according to the deconvolution sub-matrix corresponding to the sub-convolution kernel.

Abstract: An image processing method is configured to split a deconvolution kernel according to a preset splitting mode to obtain a sub-convolution kernel. And then, determining an original sub-matrix corresponding to the sub-convolution kernel, according to parameters of the sub-convolution kernel and an image feature matrix, and performing a convolution operation on the original sub-matrix corresponding to the sub-convolution kernel by using the sub-convolution kernel to obtain a deconvolution sub-matrix corresponding to each sub-convolution kernel; determining a target feature matrix according to the deconvolution sub-matrix corresponding to the sub-convolution kernel.

Abstract: The present disclosure is applied for artificial intelligence (AI) technology field and provided a method for gradient updating of an image processing model and a related apparatus thereof. The method includes: determining a convolution kernel and convoluted data corresponding to each convolution layer by invoking a direct memory access (DMA) controlling module, according to convolution parameters of each convolution layer in the image processing model, and storing the convolution kernel and the convoluted data into a first cache space and a second cache space, respectively, the convolution kernel including a convolution kernel for an original image feature gradient and a convolution kernel for an original weight gradient; and performing an inverted convolution calculation based on the convolution kernel in the first cache space and the convoluted data in the second cache space to update the original image feature gradient and the original weight gradient of each convolution layer.