Refresh Power Reduction of DRAMs in DNN Systems Using Hybrid Voting and ECC Method

Tsung Fu Hsieh, Jin Fu Li, Jenn Shiang Lai, Chih Yen Lo, Ding Ming Kwai, Yung Fa Chou

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

A deep neural network (DNN) system typically needs dynamic random access memories (DRAMs) for the data buffering. In this paper, an error-correction-code (ECC)-based technique is proposed to reduce the refresh power of DRAMs in the DNN system by extending the refresh period. By taking advantage of the characteristics of weight data of DNNs, a hybrid voting and ECC (VECC) method is used to protect the weight data from data retention fault caused by the refresh period extension. Analysis results show that the VECC method can achieve about 93% refresh power saving with about 0.5% accuracy loss and smaller than 0.5% check bit overhead on AlexNet, ResNet, and VGG19 convolutional neural network CNN models trained by ImageNet data set.

原文???core.languages.en_GB???
主出版物標題Proceedings - 2020 IEEE International Test Conference in Asia, ITC-Asia 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面41-46
頁數6
ISBN(電子)9781728189444
DOIs
出版狀態已出版 - 9月 2020
事件4th IEEE International Test Conference in Asia, ITC-Asia 2020 - Taipei, Taiwan
持續時間: 23 9月 202025 9月 2020

出版系列

名字Proceedings - 2020 IEEE International Test Conference in Asia, ITC-Asia 2020

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???event.eventtypes.event.conference???4th IEEE International Test Conference in Asia, ITC-Asia 2020
國家/地區Taiwan
城市Taipei
期間23/09/2025/09/20

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