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

• المؤلفون الرئيسيون: Hu, Jinhai, Goh, Wang Ling, Gao, Yuan
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2024
• الموضوعات: Engineering; LSTM; Fixed-point weight; Time series forecasting
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/179102; https://ieeexplore.ieee.org/abstract/document/9458508
• المؤسسة: Nanyang Technological University

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...