
A low‐cost compensated approximate multiplier for Bfloat16 data processing on convolutional neural network inference
Author(s) -
Kim HyunJin
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2020-0370
Subject(s) - multiplier (economics) , convolutional neural network , adder , multiplication (music) , computer science , inference , artificial neural network , arithmetic , approximation error , algorithm , error detection and correction , artificial intelligence , mathematics , latency (audio) , telecommunications , combinatorics , economics , macroeconomics
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 stage adopts the exact multiplication to compensate for the error from the first stage by multiplying error terms and adding its truncated result to the final output. In our design, the low‐cost multiplications in both stages can reduce hardware costs significantly and provide low relative errors by compensating for the error from the first stage. We apply our approximate multiplier to the convolutional neural network (CNN) inferences, which shows small accuracy drops with well‐known pre‐trained models for the ImageNet database. Therefore, our design allows low‐cost CNN inference systems with high test accuracy.