Abstract: A containerized immersion cooling system is provided. The system includes a container with a tank provided therein, wherein the container includes a first side wall having a first port and a second port; a coolant inlet pipe coupled to a first side of the first port; a warmed fluid pipe coupled to a first side of the second port; and a coolant distributor coupled to a second side of the first port.

Abstract: A system parses a very long instruction word (VLIW) to obtain an execution parameter. The system obtains a first sliding window width count, a first sliding window height count, a first feature map width count, and a first feature map height count that correspond to first target data. In accordance with a determination that the first sliding window width count falls within the sliding window width range, the first sliding window height count falls within the sliding window height range, (the first feature map width count falls within the feature map width range, and the first feature map height count falls within the feature map height range, the system determines an offset of the first target data. The system also obtains a starting address of the first target data, and adds the starting address to the offset to obtain a first target address of the first target data.

Abstract: A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.

Abstract: The present disclosure describes methods, devices, and storage mediums for adjusting computing resource. The method includes obtaining an expected pooling time of a target pooling layer and a to-be-processed data volume of the target pooling layer; obtaining a current clock frequency corresponding to at least one computing resource unit used for pooling; determining a target clock frequency according to the expected pooling time of the target pooling layer and the to-be-processed data volume of the target pooling layer; and in response to that the convolution layer associated with the target pooling layer completes convolution and the current clock frequency is different from the target clock frequency, switching the current clock frequency of the at least one computing resource unit to the target clock frequency, and performing pooling in the target pooling layer based on the at least one computing resource unit having the target clock frequency.

Abstract: A system parses a very long instruction word (VLIW) to obtain an execution parameter. The system obtains a first sliding window width count, a first sliding window height count, a first feature map width count, and a first feature map height count that correspond to first target data. In accordance with a determination that the first sliding window width count falls within the sliding window width range, the first sliding window height count falls within the sliding window height range, (the first feature map width count falls within the feature map width range, and the first feature map height count falls within the feature map height range, the system determines an offset of the first target data. The system also obtains a starting address of the first target data, and adds the starting address to the offset to obtain a first target address of the first target data.

Abstract: A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.

Abstract: A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.

Abstract: The present disclosure describes methods, devices, and storage mediums for adjusting computing resource. The method includes obtaining an expected pooling time of a target pooling layer and a to-be-processed data volume of the target pooling layer; obtaining a current clock frequency corresponding to at least one computing resource unit used for pooling; determining a target clock frequency according to the expected pooling time of the target pooling layer and the to-be-processed data volume of the target pooling layer; and in response to that the convolution layer associated with the target pooling layer completes convolution and the current clock frequency is different from the target clock frequency, switching the current clock frequency of the at least one computing resource unit to the target clock frequency, and performing pooling in the target pooling layer based on the at least one computing resource unit having the target clock frequency.

Abstract: A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.