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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179102 https://ieeexplore.ieee.org/abstract/document/9458508 |