Dynamically-biased fixed-point LSTM for time series processing in AIoT edge device

Dynamically-biased fixed-point LSTM for time series processing in AIoT edge device

In this paper, a Dynamically-Biased Long Short-Term Memory (DB-LSTM) neural network architecture is proposed for artificial intelligence internet of things (AIoT) applications. Different from the conventional LSTM which uses static bias, DB-LSTM adjusts the cell bias dynamically based on the previou...

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書目詳細資料
Main Authors:	Hu, Jinhai, Goh, Wang Ling, Gao, Yuan
其他作者:	School of Electrical and Electronic Engineering
格式:	Conference or Workshop Item
語言:	English
出版:	2024
主題:	Engineering LSTM Fixed-point weight Time series forecasting
在線閱讀:	https://hdl.handle.net/10356/179102 https://ieeexplore.ieee.org/abstract/document/9458508
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

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