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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル14th IEEE International NEWCAS Conference, NEWCAS 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781467389006
DOI
出版ステータスPublished - 2016 10 20
イベント14th IEEE International NEWCAS Conference, NEWCAS 2016 - Vancouver, Canada
継続期間: 2016 6 262016 6 29

出版物シリーズ

名前14th IEEE International NEWCAS Conference, NEWCAS 2016

Other

Other14th IEEE International NEWCAS Conference, NEWCAS 2016
国/地域Canada
CityVancouver
Period16/6/2616/6/29

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

フィンガープリント

「A study of a top-down error correction technique using Recurrent-Neural-Network-based learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル