The present disclosure relates to a matrix transposition device and method, and a display device. The matrix transposition device includes a first counting unit, an input module, second counting units, and a first data selection unit. The first counting unit numbers a matrix element and outputs a first signal. The input module is coupled to the first counting unit, and is input with the matrix element after receiving the first signal; each. Each column of matrix elements corresponds to one of the second counting units, each of the second counting units outputs a set of second signals, and each set of the second signals includes number information of the matrix elements in a column corresponding to the second counting unit. The first data selection unit receives the second signals in an order of columns of a matrix, and orderly outputs column elements of the matrix as row elements of a transposed matrix.

Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.

Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.

An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.

An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.

The present disclosure designs a rotation matrix-based factor graph cooperative localization algorithm. Firstly, the reasons of sudden increase in an error in an operation process of a conventional factor graph cooperative localization algorithm are analyzed; secondly, a rotation matrix is designed, and the size of a rotation angle is determined; then, a rotation matrix-based cooperative localization algorithm factor graph model is constructed, and a specific algorithm flow is designed; and finally, filtering fusion estimation is performed on position status information of a slave boat. Therefore, coordinate values of master and slave boats can be transformed within a factor graph in real time without changing the measurement accuracy of an inertial device in a system to cause the coordinates of the master and slave boats that participate in the calculation of the factor graph to be inconsistent, thereby solving the problem of sudden increase in a localization error of the conventional factor graph cooperative localization algorithm, and improving the robustness of the cooperative localization system.

The present disclosure designs a rotation matrix-based factor graph cooperative localization algorithm. Firstly, the reasons of sudden increase in an error in an operation process of a conventional factor graph cooperative localization algorithm are analyzed; secondly, a rotation matrix is designed, and the size of a rotation angle is determined; then, a rotation matrix-based cooperative localization algorithm factor graph model is constructed, and a specific algorithm flow is designed; and finally, filtering fusion estimation is performed on position status information of a slave boat. Therefore, coordinate values of master and slave boats can be transformed within a factor graph in real time without changing the measurement accuracy of an inertial device in a system to cause the coordinates of the master and slave boats that participate in the calculation of the factor graph to be inconsistent, thereby solving the problem of sudden increase in a localization error of the conventional factor graph cooperative localization algorithm, and improving the robustness of the cooperative localization system.

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

An apparatus and method for complex matrix conjugation and multiplication. For example, one embodiment of a processor comprises: a decoder to decode a complex matrix conjugation and multiplication instruction including a first source operand to identify a first complex source matrix comprising a first plurality of complex values, a second source operand to identify a second complex source matrix comprising a second plurality of complex values, and a first destination operand to identify a result matrix; execution circuitry to execute the complex matrix conjugation and multiplication instruction, the execution circuitry comprising: matrix conjugation hardware logic to determine a plurality of complex conjugate values corresponding to the first plurality of complex values; transpose hardware logic to transpose the plurality of complex conjugate values to generate a conjugate transpose matrix comprising the complex conjugate values; parallel multiplication circuitry to: multiply real values from the plurality of complex conjugate values of the conjugate transpose matrix with corresponding imaginary values from the second plurality of complex values to generate a first plurality of imaginary products, and multiply imaginary values from the plurality of complex conjugate values of the conjugate transpose matrix with corresponding real values from the second plurality of complex values to generate a second plurality of imaginary products; and addition/subtraction circuitry to add each imaginary product in the first plurality of imaginary products to a corresponding imaginary product in the second plurality of imaginary products to produce a corresponding imaginary component in the result matrix.