A low‐cost compensated approximate multiplier for Bfloat16 data processing on convolutional neural network inference

A low‐cost compensated approximate multiplier for Bfloat16 data processing on convolutional neural network inference

This paper presents a low‐cost two‐stage approximate multiplier for bfloat16 (brain floating‐point) data processing. For cost‐efficient approximate multiplication, the first stage implements Mitchell's algorithm that performs the approximate multiplication using only two adders. The second stag...

Full description

Bibliographic Details
Main Author: HyunJin Kim
Format: Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 2021-07-01
Series:ETRI Journal
Subjects:
approximate computing
bfloat16 format
convolutional neural network
logarithmic multiplier
mitchell's algorithm
Online Access:https://doi.org/10.4218/etrij.2020-0370

Internet

https://doi.org/10.4218/etrij.2020-0370

Similar Items