公开(公告)号：US20240394534A1

公开(公告)日：2024-11-28

申请号：US18795315

申请日：2024-08-06

Applicant: SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION

Inventor： Dongsuk JEON ,  Sun Woo LEE

IPC: G06N3/08

Abstract: Artificial neural network calculation method and device based on parameter quantization using hysteresis are proposed to reduce a size of an artificial neural network. The artificial neural network calculation method may comprise: determining a parameter gradient of a parameter based on a first quantization parameter value of the parameter of the artificial neural network; determining a second original parameter value of the parameter based on a first original parameter value associated with the parameter gradient and the first quantization parameter value; and determining a second quantization parameter value associated with the second original parameter value based on a result of comparing the first quantization parameter value with the second original parameter value. By applying hysteresis to parameter quantization, variability may be reduced, each parameter may be trained more stably, and the performance of a quantized model is improved.

公开(公告)号：US20240394535A1

公开(公告)日：2024-11-28

申请号：US18795391

申请日：2024-08-06

Applicant: SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION

Inventor： Dongsuk JEON ,  Sun Woo LEE

IPC: G06N3/08

Abstract: Artificial neural network performance prediction method and device according to data format are proposed. The artificial neural network performance prediction method may comprise: determining a zone and an operand of an artificial neural network that uses a candidate data format; obtaining a first parameter gradient through a first simulation of the artificial neural network on input data by applying an original data format to the operand in the zone; obtaining a second parameter gradient through a second simulation of the artificial neural network on the input data by applying the candidate data format to the operand in the zone; and determining a performance indicator according to the candidate data format based on the first parameter gradient and the second parameter gradient. Therefore, it is possible to find a low-precision data format suitable for a neural network to be trained and to perform low-precision training with high performance.