Abstract: The application relates to a method and a system for accelerating a recurrent neural network based on a Cortex-M processor, and a medium. The method comprises: setting MCR instructions and a CDP instruction according to common basic operators of the recurrent neural network, the common basic operators comprising a matrix multiplication operator, a vector arithmetic operator, a sigmoid activation operator, a tanh activation operator and a quantization operator; configuring an internal register of a recurrent neural network coprocessor through the MCR instructions; and enabling the common basic operators of the recurrent neural network through the CDP instruction on the basis of the configured internal register.

Abstract: The application relates to a method and a system for accelerating a recurrent neural network based on a Cortex-M processor, and a medium. The method includes: setting a MCR instruction and a CDP instruction according to common basic operators of the recurrent neural network, the common basic operators including a matrix multiplication operator, a vector arithmetic operator, a Sigmoid activation operator, a Tan h activation operator and a quantization operator; configuring an internal register of a recurrent neural network coprocessor through the MCR instruction; and enabling the common basic operators of the recurrent neural network through the CDP instruction on the basis of the configured internal register.

Abstract: The application relates to a convolutional neural network acceleration method and system based on a Cortex-M processor, and a medium. The method comprises: setting a MCR instruction and a CDP instruction according to common basic operators of a convolutional neural network, the common basic operators comprising a convolution operator, a Relu activation operator, a pooling operator, a table look-up operator and a quantization operator; and configuring an internal register of a convolutional neural network coprocessor through the MCR instruction, and then enabling the common basic operators of the convolutional neural network through the CDP instruction. Through the application, problems of inefficiency, high cost and inflexibility of a cyclic neural network algorithm in the execution of a processor are solved, and the basic operators needed for the cyclic neural network to be executed through a coprocessor instruction set are realized.