公开(公告)号：US20220244872A1

公开(公告)日：2022-08-04

申请号：US17595847

申请日：2021-04-22

Applicant: INSTITUTE OF MICROELECTRONICS OF THE CHINESE ACADEMY OF SCIENCES

Inventor： Feng ZHANG ,  Qiang HUO ,  Zhisheng CHEN ,  Qirui REN

IPC: G06F3/06 ,  G11C13/00

Abstract: The present disclosure discloses a storage method, a data processing method, a device and an apparatus based on a non-volatile memory, the method comprising: acquiring a weight value that needs to be stored in the non-volatile memory; determining a conductivity value corresponding to the weight value according to a first conversion method if the non-volatile memory is a high-resistance storage device; determining a conductivity value corresponding to the weight value according to a second conversion method which is different from the first conversion method if the non-volatile memory is a low-resistance memory device; and setting the non-volatile memory according to the conductivity value to store the weight value. The non-volatile memory and the data processing method, the device, and the apparatus provided by the present disclosure solve the problem of insufficient accuracy in weight values in existing non-volatile memories, realizing the technical effect of improving storage accuracy and data processing accuracy.

公开(公告)号：US20230162007A1

公开(公告)日：2023-05-25

申请号：US17753140

申请日：2021-02-22

Applicant: INSTITUTE OF MICROELECTRONICS OF THE CHINESE ACADEMY OF SCIENCES

Inventor： Feng ZHANG ,  Qiang HUO

IPC: G06N3/0464 ,  G06N3/065

CPC classification number: G06N3/0464 ,  G06N3/065

Abstract: The present disclosure discloses a method and apparatus for convolution operation of a convolutional neural network. The method comprises acquiring input voltages used for characterizing pixel values; when the input voltages are scanned through convolutional sliding windows, obtaining times of reusing of the input voltages in the convolutional sliding windows; grouping the input voltages based on a difference in the times of reusing of the input voltages; extracting the input voltages in same groups once and performing convolution calculation with convolution kernels respectively, to obtain a result corresponding to each group; obtaining a result of convolution operation based on the result corresponding to each group, to implement convolution operation in the convolutional neural network. The present disclosure reduces energy consumption during convolution operations effectively.