A study of a top-down error correction technique using Recurrent-Neural-Network-based learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A new error correction scheme based on a brain-inspired learning algorithm, called Recurrent Neural Network (RNN), is proposed for resilient and efficient intra-chip data transmission. RNN has a feature to find partially-clustered time-series data stream and predict the next input data from previous input data stream. By utilizing this feature, a novel top-down error correction approach which considers the 'context' included in the data stream and predicts original data by an acquired knowledge can be realized. In this paper, the performance of a RNN/BCH-hybrid error correction scheme for reducing the effect of false-positive detection is demonstrated through an experimental evaluation using a general purpose microprocessor.

Original languageEnglish
Title of host publication14th IEEE International NEWCAS Conference, NEWCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389006
DOIs
Publication statusPublished - 2016 Oct 20
Event14th IEEE International NEWCAS Conference, NEWCAS 2016 - Vancouver, Canada
Duration: 2016 Jun 262016 Jun 29

Publication series

Name14th IEEE International NEWCAS Conference, NEWCAS 2016

Other

Other14th IEEE International NEWCAS Conference, NEWCAS 2016
CountryCanada
CityVancouver
Period16/6/2616/6/29

Keywords

  • approximate computing
  • context-based error correction
  • deep learning
  • intelligent information processing
  • recurrent neural network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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  • Cite this

    Natsui, M., Sugaya, N., & Hanyu, T. (2016). A study of a top-down error correction technique using Recurrent-Neural-Network-based learning. In 14th IEEE International NEWCAS Conference, NEWCAS 2016 [7604786] (14th IEEE International NEWCAS Conference, NEWCAS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NEWCAS.2016.7604786