Abstract: A method and apparatus for implementing a neural network model in a heterogeneous computing platform are disclosed. The method includes partitioning a neural network model into first sub-models based on a partition standard, obtaining second sub-models by merging at least a portion of the first sub-models based on characteristics of the first sub-models, and deploying the second sub-models.

Abstract: A method and apparatus with neural network optimization are provided. A method is performed by a device storing a target network block and processing hardware that performs optimizing for the target network block, the method includes generating, by the processing hardware, an extended network block of the target network block by increasing, a number of channels of a target operation branch in the target network block to a determined number of channels, wherein the target network block includes operation branches that include the target operation branch, and wherein each operation branch includes at least one respective channel, determining importance measures of the respective operation branches, including the target operation branch with the increased number of channels, in the extended network block, and clipping a channel of the target operation branch in the extended network block, the clipping is performed according to the importance measures of the respective operation branches.

Abstract: A method of storing a data object includes dividing data of a data object into a first part of data and a second part of data, in response to receiving a writing request for a data object, the first part of data including a data field of the data object, the second part of data including a meta data of the data object; writing the first part of data into a first data file stored in a first external storage device; and writing, into a second data file stored in a second external storage device, first location information, and the second part of data, the first location information including location information of the first part of data within the first external storage device.

Abstract: A method and apparatus for determining a neural network architecture of a processor are provided. The method of determining a target neural network architecture, the method comprising obtaining a first neural network architecture, searching for the first neural network architecture based on a loss function, in response to a first search end condition not being satisfied, and determining a target neural network architecture used in a processor, based on a result of the searching, wherein the loss function is based on a processor computation cost.

Abstract: A method and apparatus for implementing a neural network model in a heterogeneous computing platform are disclosed. The method includes partitioning a neural network model into first sub-models based on a partition standard, obtaining second sub-models by merging at least a portion of the first sub-models based on characteristics of the first sub-models, and deploying the second sub-models.

Abstract: A method of storing a data object includes dividing data of a data object into a first part of data and a second part of data, in response to receiving a writing request for a data object, the first part of data including a data field of the data object, the second part of data including a meta data of the data object; writing the first part of data into a first data file stored in a first external storage device; and writing, into a second data file stored in a second external storage device, first location information, and the second part of data, the first location information including location information of the first part of data within the first external storage device.