每年專案
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??? |
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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 2020 → 25 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 |
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國家/地區 | Taiwan |
城市 | Taipei |
期間 | 23/09/20 → 25/09/20 |
指紋
深入研究「Refresh Power Reduction of DRAMs in DNN Systems Using Hybrid Voting and ECC Method」主題。共同形成了獨特的指紋。專案
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應用於人體姿勢辨識與機器人之可重組深度神經網路引擎-總計畫暨子計畫一: 應用於監督式學習之可重組深度神經網路技術(1/3)(2/3)
Li, J.-F. (PI)
1/08/20 → 31/07/21
研究計畫: Research