Abstract: The present disclosure provides a method and apparatus for deconvolving feature data using convolution hardware. The method includes: reading a feature map and deconvolution kernel into on-chip memory, and padding zeroes to the feature map; determining convolution kernels based on the deconvolution kernel; removing a row and/or column of each convolution kernel whose elements all are invalid weights, to obtain an optimized convolution kernel, and removing a corresponding row and/or column in the zero-padded feature map to obtain an corresponding optimized feature map; convolving each optimized convolution kernel with corresponding optimized feature map using the multiply-add array, to obtain convolutional outputs; and interleaving and synthesizing the convolutional outputs to obtain an interleaving synthetic output including at least a deconvolutional output corresponding to the feature map and deconvolution kernel.

Abstract: A processing method includes: obtaining an input feature map; processing the input feature map by using a dilated convolution layer of the convolutional neural network, to obtain a plurality of local feature maps; obtaining a plurality of local output feature maps by performing zero padding on the plurality of local feature maps performing convolution processing on the plurality of zero-padded local feature maps; and fusing the plurality of local output feature maps, to obtain an output feature map processed by the dilated convolution layer. A plurality of consecutive local feature maps can be split from the input feature map. The local feature map can be performed with convolution processing by using a compact convolution kernel. Performing dilated convolution processing on the input feature map under a premise of not increasing computational complexity overcomes limitation of holes on a dilated convolution algorithm, and can realize data reuse between adjacent sliding windows.

Abstract: Disclosed are a data compression method, a computer-readable storage medium, and an electronic device. The method includes: converting each data in a to-be-compressed data set into binary data in a preset format; determining a to-be-compressed bit and a significant bit for the each data in the to-be-compressed data set based on a sequence of all bits of the binary data; determining a compression bit width corresponding to the to-be-compressed data set based on bit widths of the significant bits; compressing the each data in the to-be-compressed data set based on the compression bit width, to obtain a compressed data set; and generating attribute information of the compressed data set. According to the present disclosure, the significant bit can be determined based on the sequence of all bits without adjusting orders of the bits of the binary data, thereby simplifying a data compression process and improving efficiency of data compression.

Abstract: Embodiments of the present disclosure disclose a data compression method and apparatus, a computer-readable storage medium, and an electronic device. The method includes: converting each data in a to-be-compressed data set into binary data in a preset format; determining a to-be-compressed bit and a significant bit for the each data in the to-be-compressed data set based on a sequence of all bits of the binary data; determining a compression bit width corresponding to the to-be-compressed data set based on bit widths of the significant bits; compressing the each data in the to-be-compressed data set based on the compression bit width, to obtain a compressed data set; and generating attribute information of the compressed data set. According to the embodiments of the present disclosure, the significant bit can be determined based on the sequence of the all bits without adjusting orders of the bits of the binary data.

Abstract: The present application discloses a neural network computation method includes determining the size of the first feature map obtained when the processor computes the present layer of the neural network before performing convolution computation on the next layer of the neural network; determining a convolution computation order of the next layer according to the size of the first feature map and the size of the second feature map for a convolution supported by the next layer; performing convolution computation instructions from the next layer based on the convolution computation order. Exemplary embodiments in the present disclosure decrease the interlayer feature map data access overhead and reduce the idle time of a computation unit by leaving out the storage of the first feature map and the loading process of the second feature map.