Abstract: The present disclosure provides a processing device and method. The device includes: an input/output module, a controller module, a computing module, and a storage module. The input/output module is configured to store and transmit input and output data; the controller module is configured to decode a computation instruction into a control signal to control other modules to perform operation; the computing module is configured to perform four arithmetic operation, logical operation, shift operation, and complement operation on data; and the storage module is configured to temporarily store instructions and data. The present disclosure can execute a composite scalar instruction accurately and efficiently.

Abstract: A processing device with dynamically configurable operation bit width, characterized by comprising: a memory for storing data, the data comprising data to be operated, intermediate operation result, final operation result, and data to be buffered in a neural network; a data width adjustment circuit for adjusting the width of the data to be operated, the intermediate operation result, the final operation result, and/or the data to be buffered; an operation circuit for operating the data to be operated, including performing operation on data to be operated of different bit widths by using an adder circuit and a multiplier; and a control circuit for controlling the memory, the data width adjustment circuit and the operation circuit. The device of the present disclosure can have the advantages of strong flexibility, high configurability, fast operation speed, low power consumption or the like.

Abstract: The present disclosure provides a computation method and product thereof. The computation method adopts a fusion method to perform machine learning computations. Technical effects of the present disclosure include fewer computations and less power consumption.

Abstract: Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.

Abstract: The present disclosure provides a computation method and product thereof. The computation method adopts a fusion method to perform machine learning computations. Technical effects of the present disclosure include fewer computations and less power consumption.

Abstract: The present disclosure provides a computation method and product thereof. The computation method adopts a fusion method to perform machine learning computations. Technical effects of the present disclosure include fewer computations and less power consumption.

Abstract: The present disclosure provides a computation method and product thereof. The computation method adopts a fusion method to perform machine learning computations. Technical effects of the present disclosure include fewer computations and less power consumption.

Abstract: The present disclosure provides a processing device and method. The device includes: an input/output module, a controller module, a computing module, and a storage module. The input/output module is configured to store and transmit input and output data; the controller module is configured to decode a computation instruction into a control signal to control other modules to perform operation; the computing module is configured to perform four arithmetic operation, logical operation, shift operation, and complement operation on data; and the storage module is configured to temporarily store instructions and data. The present disclosure can execute a composite scalar instruction accurately and efficiently.

Abstract: A processing device with dynamically configurable operation bit width, characterized by comprising: a memory for storing data, the data comprising data to be operated, intermediate operation result, final operation result, and data to be buffered in a neural network; a data width adjustment circuit for adjusting the width of the data to be operated, the intermediate operation result, the final operation result, and/or the data to be buffered; an operation circuit for operating the data to be operated, including performing operation on data to be operated of different bit widths by using an adder circuit and a multiplier; and a control circuit for controlling the memory, the data width adjustment circuit and the operation circuit. The device of the present disclosure can have the advantages of strong flexibility, high configurability, fast operation speed, low power consumption or the like.

Abstract: Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.

Abstract: Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.

Abstract: Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.