Dynamic point stochastic rounding algorithm for limited precision arithmetic in Deep Belief Network training
This paper reports how to train a Deep Belief Network (DBN) using only 8-bit fixed-point parameters. We propose a dynamic-point stochastic rounding algorithm that provides enhanced results compared to the existing stochastic rounding. We show that by using a variable scaling factor, the fixed-point...
| Main Authors: | Essam, M., Tang, T.B., Ho, E.T.W., Chen, H. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.20033 / |
| Published: |
IEEE Computer Society
2017
|
| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028585216&doi=10.1109%2fNER.2017.8008430&partnerID=40&md5=3d565633d6b0ee148b8349a9b15f1d76 http://eprints.utp.edu.my/20033/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|