公开(公告)号：GB2590000A

公开(公告)日：2021-06-16

申请号：GB202100363

申请日：2019-06-13

Applicant: IBM

Inventor： SWAGATH VENKATARAMANI ,  SHUBHAM JAIN ,  VIJAYALAKSHMI SRINIVASAN ,  LELAND CHANG ,  JUNGWOOK CHOI

IPC: G06N3/02

Abstract: A compensated deep neural network (compensated-DNN) is provided. A first vector having a set of components and a second vector having a set of corresponding components are received. A component of the first vector includes a first quantized value and a first compensation instruction,and a corresponding component of the second vector includes a second quantized value and a second compensation instruction. The first quantized value is multiplied with the second quantized value to compute a raw product value. The raw product value is compensated for a quantization error according to the first and second compensation instructions to produce a compensated product value. The compensated product value is added into an accumulated value for the dot product. The accumulated value is converted into an output vector of the dot product. The output vector includes an output quantized value and an output compensation instruction.

公开(公告)号：GB2590000B

公开(公告)日：2022-12-07

申请号：GB202100363

申请日：2019-06-13

Applicant: IBM

Inventor： SWAGATH VENKATARAMANI ,  SHUBHAM JAIN ,  VIJAYALAKSHMI SRINIVASAN ,  LELAND CHANG ,  JUNGWOOK CHOI

IPC: G06N3/063

Abstract: A compensated deep neural network (compensated-DNN) is provided. A first vector having a set of components and a second vector having a set of corresponding components are received. A component of the first vector includes a first quantized value and a first compensation instruction, and a corresponding component of the second vector includes a second quantized value and a second compensation instruction. The first quantized value is multiplied with the second quantized value to compute a raw product value. The raw product value is compensated for a quantization error according to the first and second compensation instructions to produce a compensated product value. The compensated product value is added into an accumulated value for the dot product. The accumulated value is converted into an output vector of the dot product. The output vector includes an output quantized value and an output compensation instruction.