公开(公告)号：US20200342301A1

公开(公告)日：2020-10-29

申请号：US16961932

申请日：2019-07-12

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Xiangshui MIAO ,  Yi LI ,  Wenqian PAN

IPC: G06N3/063 ,  G06N3/04 ,  G06N3/08 ,  G06F17/15

Abstract: Disclosed by the disclosure is a convolutional neural network on-chip learning system based on non-volatile memory, comprising: an input module, a convolutional neural network module, an output module and a weight update module. The on-chip learning of the convolutional neural network module implements a synaptic function by using a characteristic which the conductance of a memristor changes according to an applied pulse, and the convolutional kernel value or synaptic weight value is stored in a memristor unit; the input module converts an input signal into a voltage signal required by the convolutional neural network module; the convolutional neural network module converts the input voltage signal level by level, and transmits the result to the output module to obtain an output of the network; and the weight update module adjusts the conductance value of the memristor in the convolutional neural network module according to the result of the output module to update a network convolutional kernel value or synaptic weight value.

公开(公告)号：US20200285954A1

公开(公告)日：2020-09-10

申请号：US16464977

申请日：2018-06-07

Applicant: Huazhong University of Science and Technology

Inventor： Yi LI ,  Wenqian PAN ,  Xiangshui MIAO

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: The present disclosure discloses a memory-based CNN, comprising: an input module, a convolution layer circuit module, a pooling layer circuit module, an activation function module, a fully connected layer circuit module, a softmax function module and an output module, convolution kernel values or synapse weights are stored in the NOR FLASH units; the input module converts an input signal into a voltage signal required by the convolutional neural network; the convolutional layer circuit module convolves the voltage signal corresponding to the input signal with the convolution kernel values, and transmits the result to the activation function module; the activation function module activates the signal; the pooling layer circuit module performs a pooling operation on the activated signal; the fully connected layer circuit module multiplies the pooled signal with the synapse weights to achieve classification; the softmax function module normalizes the classification result into probability values as an output of the entire network. The disclosure satisfies the requirements of real-time data processing and has low hardware cost.