relation: https://khub.utp.edu.my/scholars/8456/
title: Dynamic point stochastic rounding algorithm for limited precision arithmetic in Deep Belief Network training
creator: Essam, M.
creator: Tang, T.B.
creator: Ho, E.T.W.
creator: Chen, H.
description: 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 parameter updates are enhanced. To be more hardware amenable, the use of common scaling factor at each layer of DBN is further proposed. Using publicly available MNIST database, we show that the proposed algorithm can train a 3-layer DBN with an average accuracy of 98.49, with a drop of 0.08 from the double floating-point average accuracy. © 2017 IEEE.
publisher: IEEE Computer Society
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Essam, M. and Tang, T.B. and Ho, E.T.W. and Chen, H.  (2017) Dynamic point stochastic rounding algorithm for limited precision arithmetic in Deep Belief Network training.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028585216&doi=10.1109%2fNER.2017.8008430&partnerID=40&md5=3d565633d6b0ee148b8349a9b15f1d76
relation: 10.1109/NER.2017.8008430
identifier: 10.1109/NER.2017.8008430