Abstract: Embodiments of the present disclosure provide a processing unit, a processor core, a neural network training machine and a method for processing. The method can include: acquiring a compressed weight signal; and decompressing the compressed weight signal into a weight signal and a trimming signal, wherein the weight signal comprises a weight of each neural network node, the trimming signal indicates whether the weight of each neural network node is used in a weight gradient computation, the trimming signal is used for controlling an access to an operand memory storing operands used in the weight computation of one or more neural network nodes corresponding to the operand memory, and the trimming signal is further used for controlling a computing unit to perform weight gradient computation using the weight signal and the operands for the one or more neural network nodes.

Abstract: The present disclosure discloses a method and a computing device for processing an input vector sequence to generate an output vector. The method comprises processing one or more input vectors of the input vector sequence to generate one or more sub-vectors respectively, the input vector sequence including a plurality of input vectors including the one or more input vectors arranged in a sequence, wherein the plurality of input vectors and the output vector characterize features of an object, and a respective input vector is processed by processing one or more input vectors adjacent to the respective input vector in the input vector sequence using a machine learning method to generate a corresponding sub-vector; and after sequentially generating the one or more corresponding sub-vectors for the one or more input vectors in the input vector sequence to form a sub-vector sequence including the one or more sub-vectors, generating the output vector based on the sub-vector sequence.